Location: London, United Kingdom
Workplace: hybrid
About Global University Systems
Global University Systems (GUS) is one of the world's leading education groups, bringing together a diverse network of higher education institutions, academic partners and student recruitment organisations across multiple countries.
With more than 11,000 colleagues and 36 academic institutions worldwide, we combine global scale with an entrepreneurial mindset, creating opportunities to innovate, grow and make an impact. As an ambitious international organisation, we continue to evolve, invest and expand, giving our people the chance to help shape what comes next and to change lives through education.
The Role
We are looking for a highly organised and relationship-focused Post-Acquisition Project Manager to join our growing Value Creation team.
Acquisitions are a critical part of GUS's growth strategy. This role will ensure newly acquired institutions transition smoothly from transaction completion into operational ownership, helping deliver a structured and successful integration experience.
You'll act as the central coordinator during the first stages of ownership, working across finance, legal, governance, compliance, technology, operations and leadership teams to ensure completion actions are delivered, obligations are met and integration activities stay on track.
This is a role for someone who enjoys bringing order to complexity, building relationships across multiple stakeholder groups and making sure important projects move forward at pace.
You'll have significant visibility across the organisation and will work closely with senior stakeholders involved in acquisitions and portfolio management.
What you'll be doing
Owning and coordinating post-acquisition integration plans for newly acquired institutions.
Managing post-completion activities and ensuring all transaction-related actions are completed on time.
Coordinating activity across finance, legal, regulatory, governance, compliance and operational workstreams.
Tracking completion conditions, obligations and key milestones following acquisition close.
Establishing governance processes, reporting structures and project tracking tools.
Managing project plans, dependencies, risks and issue logs.
Producing regular progress updates for senior stakeholders and leadership teams.
Supporting board, governance and regulatory transition activities where required.
Acting as a key point of contact between GUS teams and newly acquired institutions.
Supporting the handover from transaction delivery into longer-term value creation programmes.
Leading or supporting specific transformation and integration projects across the portfolio.
Driving accountability and ensuring actions are completed by relevant stakeholders.
What we're looking for
You will be an experienced project professional who is comfortable operating across multiple workstreams and stakeholder groups, particularly within acquisition, integration or transformation environments.
Ideally, you will bring:
5-10 years' experience in project management, integration, transformation, PMO or M&A environments.
Experience within a private equity-backed business, consolidator, consulting firm, corporate development team or acquisitive organisation.
Strong programme and project management skills, including planning, governance and reporting.
Experience coordinating cross-functional stakeholders across finance, legal, compliance, operations and leadership teams.
Excellent communication and relationship-building skills.
A structured approach to problem-solving and managing competing priorities.
Strong risk management and organisational skills.
The ability to bring clarity and momentum to complex projects.
Experience supporting integration, carve-out, transformation or post-merger activities.
Confidence working with senior stakeholders and executives.
Experience within education is not essential. We are particularly interested in individuals who have worked in environments where acquisitions, integrations and organisational change are a regular part of business operations.
What you'll gain
This is an opportunity to join a newly forming Value Creation team and play a key role in supporting GUS's acquisition strategy. You'll gain exposure to high-profile transactions, work closely with senior leaders and help shape how acquisitions are successfully integrated across a global education group.
What we offer:
Hybrid working -
most roles offer hybrid or flexible arrangements to support work-life balance.
Contributory Pension Scheme -
5% personal contribution & 3% Company contribution, so you can build long-term financial security with added benefits of the company contributions.
Private Medical Insurance (T&C's apply) -
prioritising your wellbeing with speedy access to healthcare whenever you need support.
25 days annual leave plus bank holidays -
time to rest, recharge, and balance life beyond work.
Season Ticket Loan (T&C's apply) -
spread the cost of your commute with an interest-free travel loan.
Eye Care Vouchers -
support for regular eye tests and essential eyewear.
Training & Development opportunities (T&C's apply) -
access to short courses via FutureLearn and Group-wide professional development programmes.
Staff Discount Scheme -
via My Rewards Hub, from various retailers – Fashion, Grocery, Health & Beauty, Travel, Automotive, Furniture, Dining, Sports and Leisure, Electronics, etc
Employee Assistance Programme -
confidential support for your wellbeing, whenever you need it.
Refer a Friend Scheme for staff members (T&C's apply) -
earn rewards for helping us bring great talent into the team
If you feel this is the role you are looking for, please click below to apply for the role!
We will screen all applicants and select candidates whose qualifications meet our requirements. We will carefully consider your application during the initial screening and will contact you if you are selected to continue for the next stage of the recruitment process. We wish you every success in your job search.
GUS is an equal opportunities employer and positively encourages applications from suitably qualified and eligible candidates regardless of sex, race, disability, age, sexual orientation, or religion or belief.
Our data privacy policy can be viewed
here
.
Location: Germany (Remote)
Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at
grafana.com
and follow us on
LinkedIn
and
X
.
We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.
You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.
This is a remote opportunity and we would be interested in applications from the UK, Ireland, Sweden, Spain and Germany.
The opportunity:
Grafana Labs is building a native experimentation platform: a Grafana app that lets teams define A/B tests, connect them to feature flags and product telemetry, and get trustworthy, statistically sound answers about what their changes actually did.
It started as a hackathon project and is now used by Grafana engineering and product teams in our internal environment to run real experiments. The next step is to make it a first-class product: one we rely on every day internally and, over time, one we offer to Grafana Cloud customers alongside Frontend Observability, Session Replay and our other user-experience capabilities.
You'll join the
Product Analytics team
, a group of highly technical product analysts who help teams across Grafana understand how our products are used and run experiments on them. As the team's dedicated software engineer, you'll own the engineering of the experimentation app and have a lot of influence over where it goes. You'll partner closely with data scientists on the statistics, data engineers on event pipelines and the data warehouse, software engineers across Grafana on feature flags, Frontend Observability and the App Platform, and product stakeholders who rely on experiment results to make decisions. This is a hands-on engineering role that ships production code. It is not an analyst role.
About the product:
The experimentation app is built on the Grafana App Platform:
Frontend:
a TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin that powers experiment statistics panels.
Backend:
Go, built with the Grafana App SDK. Experiments are Kubernetes-style custom resources defined in CUE, with an operator, admission webhooks, and support for declarative, GitOps-style management.
Feature flags:
integration with
OpenFeature
and the
OpenFeature Remote Evaluation Protocol (OFREP)
, Grafana's multi-tenant feature toggle service, and an embedded GO Feature Flag evaluator.
Statistics engine:
a Bayesian analysis engine (TypeScript, with a Go port) that produces a direction-aware verdict per metric, probability to be best, credible intervals, expected loss and sample-size recommendations.
Data:
experiment exposure and outcome events flow from product instrumentation through RudderStack into BigQuery, or from Grafana Faro into Loki, and are queried live through Grafana datasources.
AI:
experiment authoring assisted by
Grafana Assistant
, which discovers relevant data and drafts the experiment configuration for a human to review.
What You’ll be doing:
Evolve the experimentation app from internal tool to product.
Full ownership of features end to end across the React frontend and Go backend, from problem framing and design through rollout, measurement and iteration.
Build the experimentation experience.
Develop intuitive workflows for defining hypotheses, configuring experiments and metrics, managing their lifecycle, and exploring results. Make these experiences useful for product managers and analysts as well as engineers.
Make instrumentation easier to adopt and trust
. Evolve the experimentation SDK and its OpenFeature and Faro integrations. Help teams connect variant assignment, actual exposure, and outcome events with consistent identity and event contracts.
Connect experiments to the rest of Grafana’s user-experience stack.
Integrate with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, and link experiment results to Session Replay and critical user journeys so teams can see
why
a variant won, not only
that
it won.
Harden the statistics and analysis path.
Work with our data analysts to extend the statistics engine, and make analysis scalable, cost-aware and correct. Examples include moving computation server-side, scheduled analysis, alerting on experiment health, and guardrails against misleading results.
Use AI where it genuinely helps.
Extend the Grafana Assistant integration for authoring, explaining and reporting on experiments.
Raise the engineering bar.
Mentor teammates, including analysts who contribute code, through code review, pairing and design docs. Write the architecture decision records (ADRs), docs and demos that help other teams adopt experimentation.
Own what you ship.
Take part in on-call for the services you build, and support the internal teams that run experiments on them.
We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups, always with strong code review and quality standards. You’ll also have access to the latest frontier models from OpenAI, Anthropic, and Google.
What makes you a great fit:
We highly value people who are intellectually curious, default to transparency, have a strong bias towards action, and are kind and enjoy working as a team.
Strong full-stack engineering experience.
You’ve built and operated production web applications with TypeScript and React, and backend services in Go (or a similar language, with willingness to work in Go). You value maintainability, automated testing and secure development practices.
Experience building an in-house platform or integrating existing experimentation and A/B testing platform
(for example Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly experiments, or an in-house platform).
Product and data intuition.
You care whether what you shipped actually moved the needle, and you’re comfortable working with event data, SQL and data warehouses such as BigQuery.
Technical ownership.
You’ve delivered complex projects with minimal supervision, broken them into short feedback loops (analyze, design, ship an MVP, learn, iterate), and helped teammates work more effectively.
Operational responsibility.
You’ve been on call, supported software in production, and know how to make systems observable and debuggable.
Pragmatic AI usage.
You use AI coding tools thoughtfully, validate their output, and share practices that improve the team’s speed
and
quality.
Clear communication.
You can explain technical and statistical tradeoffs to engineers, analysts and product managers in a fully remote, asynchronous environment.
Bonus Points For:
Hands-on experience with feature flags and OpenFeature.
A working understanding of experiment statistics: Bayesian or frequentist testing, sample sizing, variance reduction, and common pitfalls such as peeking and sample ratio mismatch.
Experience with Kubernetes concepts: custom resources, operators, admission webhooks, GitOps.
Experience building Grafana plugins or apps, or with the Grafana App SDK or Scenes.
Familiarity with frontend observability and product analytics tooling, such as Grafana Faro, RUM, session replay, RudderStack or Segment.
Open source contribution or maintainership.
Experience as a user of Grafana, Prometheus, Loki or Tempo, whether at work or on a homelab.
Compensation & Rewards:
In Germany, the base compensation range for this role is €
100,000 - €125,000
. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally.
*Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process.
Why You’ll Thrive at Grafana Labs:
100% Remote, Global Culture -
As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose.
Scaling Organization
– Tackle meaningful work in a high-growth, ever-evolving environment.
Transparent Communication
– Expect open decision-making and regular company-wide updates.
Innovation-Driven
– Autonomy and support to ship great work and try new things.
Open Source Roots
– Built on community-driven values that shape how we work.
Empowered Teams
– High trust, low ego culture that values outcomes over optics.
Career Growth Pathways
– Defined opportunities to grow and develop your career.
Approachable Leadership
– Transparent execs who are involved, visible, and human.
Passionate People
– Join a team of smart, supportive folks who care deeply about what they do.
In-Person onboarding
- We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it.
Balance is Key
- We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect.
*We will comply with local legislation where applicable.
Equal Opportunity Employer:
Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow.
Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings.
#LI-Remote
For information about how your personal data is used once you’ve applied to a job, check out our
privacy policy
.
Location: Spain (Remote)
Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at
grafana.com
and follow us on
LinkedIn
and
X
.
We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.
You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.
This is a remote opportunity and we would be interested in applications from the UK, Ireland, Sweden, Spain and Germany.
The opportunity:
Grafana Labs is building a native experimentation platform: a Grafana app that lets teams define A/B tests, connect them to feature flags and product telemetry, and get trustworthy, statistically sound answers about what their changes actually did.
It started as a hackathon project and is now used by Grafana engineering and product teams in our internal environment to run real experiments. The next step is to make it a first-class product: one we rely on every day internally and, over time, one we offer to Grafana Cloud customers alongside Frontend Observability, Session Replay and our other user-experience capabilities.
You'll join the
Product Analytics team
, a group of highly technical product analysts who help teams across Grafana understand how our products are used and run experiments on them. As the team's dedicated software engineer, you'll own the engineering of the experimentation app and have a lot of influence over where it goes. You'll partner closely with data scientists on the statistics, data engineers on event pipelines and the data warehouse, software engineers across Grafana on feature flags, Frontend Observability and the App Platform, and product stakeholders who rely on experiment results to make decisions. This is a hands-on engineering role that ships production code. It is not an analyst role.
About the product:
The experimentation app is built on the Grafana App Platform:
Frontend:
a TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin that powers experiment statistics panels.
Backend:
Go, built with the Grafana App SDK. Experiments are Kubernetes-style custom resources defined in CUE, with an operator, admission webhooks, and support for declarative, GitOps-style management.
Feature flags:
integration with
OpenFeature
and the
OpenFeature Remote Evaluation Protocol (OFREP)
, Grafana's multi-tenant feature toggle service, and an embedded GO Feature Flag evaluator.
Statistics engine:
a Bayesian analysis engine (TypeScript, with a Go port) that produces a direction-aware verdict per metric, probability to be best, credible intervals, expected loss and sample-size recommendations.
Data:
experiment exposure and outcome events flow from product instrumentation through RudderStack into BigQuery, or from Grafana Faro into Loki, and are queried live through Grafana datasources.
AI:
experiment authoring assisted by
Grafana Assistant
, which discovers relevant data and drafts the experiment configuration for a human to review.
What You’ll be doing:
Evolve the experimentation app from internal tool to product.
Full ownership of features end to end across the React frontend and Go backend, from problem framing and design through rollout, measurement and iteration.
Build the experimentation experience.
Develop intuitive workflows for defining hypotheses, configuring experiments and metrics, managing their lifecycle, and exploring results. Make these experiences useful for product managers and analysts as well as engineers.
Make instrumentation easier to adopt and trust
. Evolve the experimentation SDK and its OpenFeature and Faro integrations. Help teams connect variant assignment, actual exposure, and outcome events with consistent identity and event contracts.
Connect experiments to the rest of Grafana’s user-experience stack.
Integrate with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, and link experiment results to Session Replay and critical user journeys so teams can see
why
a variant won, not only
that
it won.
Harden the statistics and analysis path.
Work with our data analysts to extend the statistics engine, and make analysis scalable, cost-aware and correct. Examples include moving computation server-side, scheduled analysis, alerting on experiment health, and guardrails against misleading results.
Use AI where it genuinely helps.
Extend the Grafana Assistant integration for authoring, explaining and reporting on experiments.
Raise the engineering bar.
Mentor teammates, including analysts who contribute code, through code review, pairing and design docs. Write the architecture decision records (ADRs), docs and demos that help other teams adopt experimentation.
Own what you ship.
Take part in on-call for the services you build, and support the internal teams that run experiments on them.
We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups, always with strong code review and quality standards. You’ll also have access to the latest frontier models from OpenAI, Anthropic, and Google.
What makes you a great fit:
We highly value people who are intellectually curious, default to transparency, have a strong bias towards action, and are kind and enjoy working as a team.
Strong full-stack engineering experience.
You’ve built and operated production web applications with TypeScript and React, and backend services in Go (or a similar language, with willingness to work in Go). You value maintainability, automated testing and secure development practices.
Experience building an in-house platform or integrating existing experimentation and A/B testing platform
(for example Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly experiments, or an in-house platform).
Product and data intuition.
You care whether what you shipped actually moved the needle, and you’re comfortable working with event data, SQL and data warehouses such as BigQuery.
Technical ownership.
You’ve delivered complex projects with minimal supervision, broken them into short feedback loops (analyze, design, ship an MVP, learn, iterate), and helped teammates work more effectively.
Operational responsibility.
You’ve been on call, supported software in production, and know how to make systems observable and debuggable.
Pragmatic AI usage.
You use AI coding tools thoughtfully, validate their output, and share practices that improve the team’s speed
and
quality.
Clear communication.
You can explain technical and statistical tradeoffs to engineers, analysts and product managers in a fully remote, asynchronous environment.
Bonus Points For:
Hands-on experience with feature flags and OpenFeature.
A working understanding of experiment statistics: Bayesian or frequentist testing, sample sizing, variance reduction, and common pitfalls such as peeking and sample ratio mismatch.
Experience with Kubernetes concepts: custom resources, operators, admission webhooks, GitOps.
Experience building Grafana plugins or apps, or with the Grafana App SDK or Scenes.
Familiarity with frontend observability and product analytics tooling, such as Grafana Faro, RUM, session replay, RudderStack or Segment.
Open source contribution or maintainership.
Experience as a user of Grafana, Prometheus, Loki or Tempo, whether at work or on a homelab.
Compensation & Rewards:
In Spain, the base compensation range for this role is €85
,000 - €107,000
. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally.
*Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process.
Why You’ll Thrive at Grafana Labs:
100% Remote, Global Culture -
As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose.
Scaling Organization
– Tackle meaningful work in a high-growth, ever-evolving environment.
Transparent Communication
– Expect open decision-making and regular company-wide updates.
Innovation-Driven
– Autonomy and support to ship great work and try new things.
Open Source Roots
– Built on community-driven values that shape how we work.
Empowered Teams
– High trust, low ego culture that values outcomes over optics.
Career Growth Pathways
– Defined opportunities to grow and develop your career.
Approachable Leadership
– Transparent execs who are involved, visible, and human.
Passionate People
– Join a team of smart, supportive folks who care deeply about what they do.
In-Person onboarding
- We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it.
Balance is Key
- We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect.
*We will comply with local legislation where applicable.
Equal Opportunity Employer:
Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow.
Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings.
#LI-Remote
For information about how your personal data is used once you’ve applied to a job, check out our
privacy policy
.
Location: Republic of Ireland (Remote)
Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at
grafana.com
and follow us on
LinkedIn
and
X
.
We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.
You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.
This is a remote opportunity and we would be interested in applications from the UK, Ireland, Sweden, Spain and Germany.
The opportunity:
Grafana Labs is building a native experimentation platform: a Grafana app that lets teams define A/B tests, connect them to feature flags and product telemetry, and get trustworthy, statistically sound answers about what their changes actually did.
It started as a hackathon project and is now used by Grafana engineering and product teams in our internal environment to run real experiments. The next step is to make it a first-class product: one we rely on every day internally and, over time, one we offer to Grafana Cloud customers alongside Frontend Observability, Session Replay and our other user-experience capabilities.
You'll join the
Product Analytics team
, a group of highly technical product analysts who help teams across Grafana understand how our products are used and run experiments on them. As the team's dedicated software engineer, you'll own the engineering of the experimentation app and have a lot of influence over where it goes. You'll partner closely with data scientists on the statistics, data engineers on event pipelines and the data warehouse, software engineers across Grafana on feature flags, Frontend Observability and the App Platform, and product stakeholders who rely on experiment results to make decisions. This is a hands-on engineering role that ships production code. It is not an analyst role.
About the product:
The experimentation app is built on the Grafana App Platform:
Frontend:
a TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin that powers experiment statistics panels.
Backend:
Go, built with the Grafana App SDK. Experiments are Kubernetes-style custom resources defined in CUE, with an operator, admission webhooks, and support for declarative, GitOps-style management.
Feature flags:
integration with
OpenFeature
and the
OpenFeature Remote Evaluation Protocol (OFREP)
, Grafana's multi-tenant feature toggle service, and an embedded GO Feature Flag evaluator.
Statistics engine:
a Bayesian analysis engine (TypeScript, with a Go port) that produces a direction-aware verdict per metric, probability to be best, credible intervals, expected loss and sample-size recommendations.
Data:
experiment exposure and outcome events flow from product instrumentation through RudderStack into BigQuery, or from Grafana Faro into Loki, and are queried live through Grafana datasources.
AI:
experiment authoring assisted by
Grafana Assistant
, which discovers relevant data and drafts the experiment configuration for a human to review.
What You’ll be doing:
Evolve the experimentation app from internal tool to product.
Full ownership of features end to end across the React frontend and Go backend, from problem framing and design through rollout, measurement and iteration.
Build the experimentation experience.
Develop intuitive workflows for defining hypotheses, configuring experiments and metrics, managing their lifecycle, and exploring results. Make these experiences useful for product managers and analysts as well as engineers.
Make instrumentation easier to adopt and trust
. Evolve the experimentation SDK and its OpenFeature and Faro integrations. Help teams connect variant assignment, actual exposure, and outcome events with consistent identity and event contracts.
Connect experiments to the rest of Grafana’s user-experience stack.
Integrate with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, and link experiment results to Session Replay and critical user journeys so teams can see
why
a variant won, not only
that
it won.
Harden the statistics and analysis path.
Work with our data analysts to extend the statistics engine, and make analysis scalable, cost-aware and correct. Examples include moving computation server-side, scheduled analysis, alerting on experiment health, and guardrails against misleading results.
Use AI where it genuinely helps.
Extend the Grafana Assistant integration for authoring, explaining and reporting on experiments.
Raise the engineering bar.
Mentor teammates, including analysts who contribute code, through code review, pairing and design docs. Write the architecture decision records (ADRs), docs and demos that help other teams adopt experimentation.
Own what you ship.
Take part in on-call for the services you build, and support the internal teams that run experiments on them.
We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups, always with strong code review and quality standards. You’ll also have access to the latest frontier models from OpenAI, Anthropic, and Google.
What makes you a great fit:
We highly value people who are intellectually curious, default to transparency, have a strong bias towards action, and are kind and enjoy working as a team.
Strong full-stack engineering experience.
You’ve built and operated production web applications with TypeScript and React, and backend services in Go (or a similar language, with willingness to work in Go). You value maintainability, automated testing and secure development practices.
Experience building an in-house platform or integrating existing experimentation and A/B testing platform
(for example Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly experiments, or an in-house platform).
Product and data intuition.
You care whether what you shipped actually moved the needle, and you’re comfortable working with event data, SQL and data warehouses such as BigQuery.
Technical ownership.
You’ve delivered complex projects with minimal supervision, broken them into short feedback loops (analyze, design, ship an MVP, learn, iterate), and helped teammates work more effectively.
Operational responsibility.
You’ve been on call, supported software in production, and know how to make systems observable and debuggable.
Pragmatic AI usage.
You use AI coding tools thoughtfully, validate their output, and share practices that improve the team’s speed
and
quality.
Clear communication.
You can explain technical and statistical tradeoffs to engineers, analysts and product managers in a fully remote, asynchronous environment.
Bonus Points For:
Hands-on experience with feature flags and OpenFeature.
A working understanding of experiment statistics: Bayesian or frequentist testing, sample sizing, variance reduction, and common pitfalls such as peeking and sample ratio mismatch.
Experience with Kubernetes concepts: custom resources, operators, admission webhooks, GitOps.
Experience building Grafana plugins or apps, or with the Grafana App SDK or Scenes.
Familiarity with frontend observability and product analytics tooling, such as Grafana Faro, RUM, session replay, RudderStack or Segment.
Open source contribution or maintainership.
Experience as a user of Grafana, Prometheus, Loki or Tempo, whether at work or on a homelab.
Compensation & Rewards:
In Ireland, the base compensation range for this role is
EUR 107,000
- EUR 134,000
.
Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally.
*Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process.
Why You’ll Thrive at Grafana Labs:
100% Remote, Global Culture -
As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose.
Scaling Organization
– Tackle meaningful work in a high-growth, ever-evolving environment.
Transparent Communication
– Expect open decision-making and regular company-wide updates.
Innovation-Driven
– Autonomy and support to ship great work and try new things.
Open Source Roots
– Built on community-driven values that shape how we work.
Empowered Teams
– High trust, low ego culture that values outcomes over optics.
Career Growth Pathways
– Defined opportunities to grow and develop your career.
Approachable Leadership
– Transparent execs who are involved, visible, and human.
Passionate People
– Join a team of smart, supportive folks who care deeply about what they do.
In-Person onboarding
- We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it.
Balance is Key
- We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect.
*We will comply with local legislation where applicable.
Equal Opportunity Employer:
Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow.
Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings.
#LI-Remote
For information about how your personal data is used once you’ve applied to a job, check out our
privacy policy
.
Location: United Kingdom (Remote)
Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at
grafana.com
and follow us on
LinkedIn
and
X
.
We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.
You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.
This is a remote opportunity and we would be interested in applications from the UK, Ireland, Sweden, Spain and Germany.
The opportunity:
Grafana Labs is building a native experimentation platform: a Grafana app that lets teams define A/B tests, connect them to feature flags and product telemetry, and get trustworthy, statistically sound answers about what their changes actually did.
It started as a hackathon project and is now used by Grafana engineering and product teams in our internal environment to run real experiments. The next step is to make it a first-class product: one we rely on every day internally and, over time, one we offer to Grafana Cloud customers alongside Frontend Observability, Session Replay and our other user-experience capabilities.
You'll join the
Product Analytics team
, a group of highly technical product analysts who help teams across Grafana understand how our products are used and run experiments on them. As the team's dedicated software engineer, you'll own the engineering of the experimentation app and have a lot of influence over where it goes. You'll partner closely with data scientists on the statistics, data engineers on event pipelines and the data warehouse, software engineers across Grafana on feature flags, Frontend Observability and the App Platform, and product stakeholders who rely on experiment results to make decisions. This is a hands-on engineering role that ships production code. It is not an analyst role.
About the product:
The experimentation app is built on the Grafana App Platform:
Frontend:
a TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin that powers experiment statistics panels.
Backend:
Go, built with the Grafana App SDK. Experiments are Kubernetes-style custom resources defined in CUE, with an operator, admission webhooks, and support for declarative, GitOps-style management.
Feature flags:
integration with
OpenFeature
and the
OpenFeature Remote Evaluation Protocol (OFREP)
, Grafana's multi-tenant feature toggle service, and an embedded GO Feature Flag evaluator.
Statistics engine:
a Bayesian analysis engine (TypeScript, with a Go port) that produces a direction-aware verdict per metric, probability to be best, credible intervals, expected loss and sample-size recommendations.
Data:
experiment exposure and outcome events flow from product instrumentation through RudderStack into BigQuery, or from Grafana Faro into Loki, and are queried live through Grafana datasources.
AI:
experiment authoring assisted by
Grafana Assistant
, which discovers relevant data and drafts the experiment configuration for a human to review.
What You’ll be doing:
Evolve the experimentation app from internal tool to product.
Full ownership of features end to end across the React frontend and Go backend, from problem framing and design through rollout, measurement and iteration.
Build the experimentation experience.
Develop intuitive workflows for defining hypotheses, configuring experiments and metrics, managing their lifecycle, and exploring results. Make these experiences useful for product managers and analysts as well as engineers.
Make instrumentation easier to adopt and trust
. Evolve the experimentation SDK and its OpenFeature and Faro integrations. Help teams connect variant assignment, actual exposure, and outcome events with consistent identity and event contracts.
Connect experiments to the rest of Grafana’s user-experience stack.
Integrate with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, and link experiment results to Session Replay and critical user journeys so teams can see
why
a variant won, not only
that
it won.
Harden the statistics and analysis path.
Work with our data analysts to extend the statistics engine, and make analysis scalable, cost-aware and correct. Examples include moving computation server-side, scheduled analysis, alerting on experiment health, and guardrails against misleading results.
Use AI where it genuinely helps.
Extend the Grafana Assistant integration for authoring, explaining and reporting on experiments.
Raise the engineering bar.
Mentor teammates, including analysts who contribute code, through code review, pairing and design docs. Write the architecture decision records (ADRs), docs and demos that help other teams adopt experimentation.
Own what you ship.
Take part in on-call for the services you build, and support the internal teams that run experiments on them.
We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups, always with strong code review and quality standards. You’ll also have access to the latest frontier models from OpenAI, Anthropic, and Google.
What makes you a great fit:
We highly value people who are intellectually curious, default to transparency, have a strong bias towards action, and are kind and enjoy working as a team.
Strong full-stack engineering experience.
You’ve built and operated production web applications with TypeScript and React, and backend services in Go (or a similar language, with willingness to work in Go). You value maintainability, automated testing and secure development practices.
Experience building an in-house platform or integrating existing experimentation and A/B testing platform
(for example Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly experiments, or an in-house platform).
Product and data intuition.
You care whether what you shipped actually moved the needle, and you’re comfortable working with event data, SQL and data warehouses such as BigQuery.
Technical ownership.
You’ve delivered complex projects with minimal supervision, broken them into short feedback loops (analyze, design, ship an MVP, learn, iterate), and helped teammates work more effectively.
Operational responsibility.
You’ve been on call, supported software in production, and know how to make systems observable and debuggable.
Pragmatic AI usage.
You use AI coding tools thoughtfully, validate their output, and share practices that improve the team’s speed
and
quality.
Clear communication.
You can explain technical and statistical tradeoffs to engineers, analysts and product managers in a fully remote, asynchronous environment.
Bonus Points For:
Hands-on experience with feature flags and OpenFeature.
A working understanding of experiment statistics: Bayesian or frequentist testing, sample sizing, variance reduction, and common pitfalls such as peeking and sample ratio mismatch.
Experience with Kubernetes concepts: custom resources, operators, admission webhooks, GitOps.
Experience building Grafana plugins or apps, or with the Grafana App SDK or Scenes.
Familiarity with frontend observability and product analytics tooling, such as Grafana Faro, RUM, session replay, RudderStack or Segment.
Open source contribution or maintainership.
Experience as a user of Grafana, Prometheus, Loki or Tempo, whether at work or on a homelab.
Compensation & Rewards:
In the UK, the base compensation range for this role is £94
,000 - £118,000
. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally.
*Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process.
Why You’ll Thrive at Grafana Labs:
100% Remote, Global Culture -
As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose.
Scaling Organization
– Tackle meaningful work in a high-growth, ever-evolving environment.
Transparent Communication
– Expect open decision-making and regular company-wide updates.
Innovation-Driven
– Autonomy and support to ship great work and try new things.
Open Source Roots
– Built on community-driven values that shape how we work.
Empowered Teams
– High trust, low ego culture that values outcomes over optics.
Career Growth Pathways
– Defined opportunities to grow and develop your career.
Approachable Leadership
– Transparent execs who are involved, visible, and human.
Passionate People
– Join a team of smart, supportive folks who care deeply about what they do.
In-Person onboarding
- We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it.
Balance is Key
- We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect.
*We will comply with local legislation where applicable.
Equal Opportunity Employer:
Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow.
Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings.
#LI-Remote
For information about how your personal data is used once you’ve applied to a job, check out our
privacy policy
.
Location: London
Workplace: OnSite
Company Description
We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.
We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.
Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.
If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.
With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.
The role
As a Data Science Manager in the Payments Performance pillar you will lead a team of smart and ambitious data scientists that drive the success of our machine learning solution for payment optimization. We are looking for someone passionate about building high-performing teams that consistently deliver value in a rapidly evolving area.
You’ll be working with data scientists, analysts and engineers on creating technical solutions and communicating effectively across teams and senior leadership. Your leadership will be pivotal for translating complex problems into actionable roadmaps that ultimately impact millions of online shoppers worldwide.
How you’ll make an impact
- Drive the roadmap and priorities for your team, and work with many leaders across the company to enhance our ability to deliver best-in-class performance, through ML.
- Build the brain behind mission-critical products that make real-time decisions during the payment processing flow.
- Collaborate with stakeholders across the organization such as engineering, analytics, operations and product.
- Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
What we’re looking for
- You have at least 2 years of direct management experience leading data science and ML teams in a complex domain.
- Strong background as a Data Scientist (5+ years of proven experience)
- You’ve managed teams that have built and shipped machine learning systems and data products at scale.
- You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
- You thrive on a high level of autonomy and responsibility.
- You foster a healthy, inclusive, challenging, and supportive work environment.
- Growth mindset, always on the lookout for stretch challenges.
- Excellent communication skills, ability to simplify machine learning concepts and data analyses for broad audiences.
- Experience dealing with data from complex domains (FinTech, Fraud Detection or Payments Processing are a significant plus)
- A PhD or MS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering) is considered a plus.
Additional Information
Bring all of you to work
We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.
Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.
We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.
It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout.com http://Checkout.com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us.
For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/
Location: London
DRW
is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.
Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.
We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.
DRW
is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
As a
Senior HR Technology Analyst
, you will be a hybrid functional and technical subject matter specialist on the HR Technology team—pairing hands-on Workday configuration with strong reporting and analytics skills. You will administer and configure DRW's global HR system, Workday, while also partnering with our HR Reporting Analyst as a responsible owner of scheduled and ad hoc reporting needs across Workday, business intelligence tools, and emerging AI-enabled analytics. You will partner closely with HR and business stakeholders on system support, survey programs, and a variety of business-facing projects that improve employee experience and operational effectiveness.
Core Duties:
HR Technology & Workday Configuration
Administer and configure Workday HCM across modules such as Core HCM, Payroll, Benefits, Time Tracking, Absence, Compensation, Performance, and Talent, including troubleshooting, processing changes, business process configuration, security maintenance, integration monitoring, EIB creation and loading, and functional testing
Provide senior-level functional support for end users, diagnosing issues, identifying root causes, and delivering durable solutions
Partner with HR stakeholders to gather requirements, translate business needs into Workday configuration, and deliver scalable process improvements that support DRW's growth and employee experience
Support Workday's semi-annual system upgrades, including release note review, design recommendations, configuration, and user acceptance testing
Develop user guides, system documentation, and training materials; assist with Workday training as needed
Maintain HRIS data integrity through audits, analysis, and ongoing data quality practices
Serve as a backup for Workday permissions, security administration, and related system governance
Remain current on HR technology and data strategies, including opportunities to leverage Workday, integrations, BI, and AI tools for operational effectiveness
Reporting, Analytics & Insights
Partner with the HR Reporting Analyst as a responsible owner of Workday reporting, calculated fields, dashboards, and related analytics
Own and execute regular scheduled reporting, and support ad hoc reporting requests for HR and business stakeholders
Build, maintain, and improve reports and dashboards in Workday; support reporting needs in adjacent systems (for example, Greenhouse) as needed
Use business intelligence tools and AI-enabled analytics tools to extract, analyze, and present workforce insights that support decision-making
Help maintain the catalog of reports, dashboards, and analytics assets, ensuring accuracy, documentation, and timely delivery
Partner with stakeholders to define reporting requirements, manage expectations, and continuously improve reporting structure and data quality
Survey Support & Business-Facing Projects
Support engagement, pulse, and other business survey programs end to end—including setup, administration, reporting, and stakeholder follow-up as needed
Partner with HR and business leaders on survey design, distribution, results packaging, and related action-planning support
Lead or contribute to miscellaneous business-facing HR Technology projects, balancing configuration work, reporting deliverables, and stakeholder communication
Support broader HR Operations and HR Technology initiatives as needed, bringing both systems and analytics perspectives to project work
Required Skills & Experience:
Bachelor's degree
5–7+ years of progressive HRIS / HR Technology experience, with substantial hands-on Workday functional configuration experience in one or more of: HCM, Compensation, Payroll, Benefits, Talent, Time Tracking, Absence Management, Security, and Reporting
Demonstrated Workday configuration and implementation experience, with the ability to translate business needs into technical requirements and deliver production-ready solutions
Strong Workday reporting experience, including designing, building, testing, and maintaining custom reports, calculated fields, and dashboards
Experience owning scheduled reporting cycles and partnering on reporting deliverables in a deadline-driven environment
Proficiency with business intelligence / analytics tools (for example, Tableau, Power BI, or similar); comfort exploring and applying AI tools to accelerate analysis, documentation, and insight generation
Demonstrated ability to use AI tools thoughtfully and effectively for analysis, reporting support, documentation, and problem-solving, with good judgment about when AI adds value and when human review is required
Advanced Microsoft Excel skills, including extracting, manipulating, and validating data to produce accurate, actionable outputs
Experience supporting survey programs (engagement, pulse, or other business surveys) preferred
Strong understanding of HR business processes and operational analysis across areas such as payroll, benefits, compensation, recruiting, talent, and performance management
Excellent communication, stakeholder management, and customer service skills, with the ability to work effectively with both technical and non-technical partners
Strong organization, prioritization, and project management skills, with proven ability to balance concurrent configuration, reporting, and project work
High attention to detail, sound judgment, and experience handling highly confidential information with discretion
Ability to thrive in a fast-paced environment, adapt to changing priorities, and collaborate broadly across the organization
For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at
https://drw.com/privacy-notice
.
California residents, please review the California Privacy Notice for information about certain legal rights at
https://drw.com/california-privacy-notice
.
#LI-AA1
Location: Kyiv, Ukraine
Hi!
We are
Boosta, a holding tech company
that creates, develops, and invests in digital businesses with global potential. Today, we collaborate with over 800 specialists from different parts of the world, and millions of users use our products and services.
We are currently looking for a
Content Lead
to join our partner company.
What you will do:
Shape, develop, and regularly adapt the project’s content strategy based on business goals and analytics data.
Analyze content performance using analytics tools, identify effective and underperforming materials, find growth opportunities, and initiate relevant changes.
Regularly track key content metrics, draw conclusions, and plan next steps to improve results.
Organize and optimize content processes: build the workflow structure, assign tasks, and monitor their completion and adherence to deadlines.
Manage the content team: prioritize tasks, track performance, identify issues in a timely manner, and help the team achieve its goals.
Find, engage, and manage external contractors, monitoring the quality and timeliness of their work.
Plan and manage content budgets: build the budget, justify expenses, and control spending.
Control the quality of text and visual content, ensuring materials meet project standards.
Ensure compliance with basic SEO requirements when creating and optimizing content.
What we expect from you:
3+ years of experience working with content.
Experience managing a content team, distributing workload, setting tasks, and monitoring results.
Understanding of effective content principles and the ability to evaluate content performance based on data rather than subjective perception alone.
Hands-on experience with analytics tools.
Ability to work with content metrics, interpret analytics results, and make decisions on further content strategy.
Skills in building, systematizing, and optimizing content processes. Ability to work in a structured way, plan, and follow established processes.
Experience finding and managing external contractors.
Experience planning and controlling budgets.
Basic understanding of SEO: working with keywords, internal linking, alt tags, and other SEO content optimization principles.
Excellent written communication skills and the ability to independently create and edit high-quality texts.
Strong visual taste, understanding of quality design principles, image selection, and content formatting.
Strong communication and organizational skills, responsibility, proactivity, and a results-oriented mindset.
Your journey with us:
Step 1: Pre-screen and portfolio review.
Step 2: Interview.
Step 3: Additional interview (if needed).
Step 4: Job Offer!
Location: New York City Metropolitan Area; New Jersey
Workplace: hybrid
About the Role:
The problem you'd be solving
We build AI systems that enterprises run their business on. Production systems inside banks, insurers, and health systems, where our engineers embed with the client, learn the work, and own the outcome. We call them Forward Deployed Engineers and Forward Deployed Executives. These people hold design conversations with a Chief Data Officer on Tuesday and ship to production next Monday.
We are hiring a People & Talent Partner to help us scale our FDE/FDX, Product, and GTM teams. Five open reqs at a time, none of them generic. We would rather you run five properly than twenty badly.
Beyond filling roles, you will own the talent community strategy around Provectus, and you will build the people-operations presence for our US team locally — be the person our US colleagues come to first.
Location: Hybrid, New York: 2-3 days onsite.
Type: Full-time (W2).
What You'll Do:
Recruiting:
Full-cycle hiring across the US for Forward Deployed Engineers (software, AI/ML, architects, leads), Forward Deployed Executives, Product Owners, and Business Development Managers. Intake to offer:
Partnership with the hiring managers. You'll run calibration with them and bring market data when a scorecard doesn't match what exists.
Architect and execute Provectus's talent community strategy in the US. Meetups, workshops, hackathons, university programs, and the AI/ML communities. You don't need to have run events before. You need to want to help with that, and there's a budget and support from marketing when you do.
US hiring strategy. Talent market intelligence, compensation benchmarking, and how we position Provectus against the companies competing for the same engineers.
People operations for the US team.
You'll be the on-the-ground people partner for our US-based colleagues, working with the Head of People Operations:
Onboarding of US hires.
First point of contact for US employees on benefits, time off, policies, and day-to-day people questions.
Support for the performance review cycle for US-based staff: scheduling, collecting feedback, tracking outcomes.
Team engagement and onsite days — the rhythm that makes a hybrid New York team feel like a team.
What's the split between Recruiting and HR?
70/30
What You'll Bring:
4+ years of full-cycle recruiting experience in a fast-paced technical environment, ideally at an AI, cloud, or consulting firm
Proven track record of sourcing and hiring senior technical profiles — AI/ML engineers, software engineers, architects, or consulting leaders
Demonstrates personal AI adoption — uses AI tools as part of their recruiting workflow, not just aware of them
Exposure to HR generalist or people-operations work — onboarding, benefits, employee relations — or a clear appetite to build it. You don't need to have been an HRBP.
Excellent communication and stakeholder management skills; comfortable partnering with C-level and senior technical leadership
Highly organized, self-directed, and comfortable with ambiguity in a distributed, global team
Commitment to Provectus's core values: Obsession, Ownership, Transparency, and Partnership
Based in the New York / New Jersey area and able to be onsite [2-3] days a week
Nice to have:
Experience building talent community: events, meetups, professional networks, or university programs
Familiarity with US benefits administration, onboarding, and experience with an HRIS.
Where this goes:
This is a growing US function. The person who does this well takes on more of the community mandate, more senior searches, a deeper people-partner role as the US team grows, and a path toward owning the US talent function. We'll talk about that trajectory.
What We Offer:
Direct impact on Provectus's growth in the US market with direct visibility to leadership.
Access to the Provectus Learning Hub, Claude Code certification support
Collaborative culture that rewards ownership, curiosity, and continuous learning
Full-time W2 model.
Base $150k- 170k. Final offer reflects experience, skills, and market data.
Health, vision, dental, disability, and life insurance; 401(k) plan; unlimited vacation.
About Provectus:
Provectus is an AWS Premier Partner and an Anthropic Strategic Partner working at the front edge of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable outcomes through bespoke applications, managed services, and advisory work. We focus on Financial Services & Insurance and Healthcare & Life Sciences, with offices across North America, LATAM, and EMEA.
Hiring Process:
30-45-minute call with the Head of Talent Acquisition
30-minute call with the Head of People Operations
On-site meeting with the CEO
Location: United States - Remote
Please note that we are unable to consider applications from candidates based in France, Italy, or Germany for this role.
Money is moving onto the internet, and the shift has a name:
Open Money.
This is money that is open, portable, agentic, and owned by you. MetaMask spent the last ten years building it; with 100M+ downloads, users in ~190 countries, and trillions in cumulative transaction volume, it's the most trusted self-custodial financial platform on the internet. Now we're building the operating system for your money: one place to hold, move, grow, and use anything you own. Join a remote-first, global team rebuilding how money works: a problem that touches everyone, every day.
What you'll do
As a Senior Software Engineer, you will:
Architect, build, and maintain features related to digital assets across MetaMask Clients, including ERC-20 tokens, NFTs, Token Lists, and Token Detail Pages
Develop and support core wallet controllers that manage asset state and data throughout the MetaMask ecosystem
Expand asset support to include emerging standards such as Real World Assets (RWAs)
Help create intuitive, efficient, and secure Web3 interactions that delight users
Collaborate with product managers, designers, and other engineers to deliver high-quality features
Participate in code reviews and help maintain high code quality standards
Requirements:
3+ years working with mobile-based technologies
3+ years building websites and/or applications
Experience with React and React Native
Strong knowledge of modern Javascript (ES6/TypeScript/etc.)
Familiarity with blockchain fundamentals and Web3 integration
Working knowledge of cross platform/browser compatibility
Experience working with APIs and middleware services
Experience working in an agile development team
Experience developing applications with clean code practices, testing, and adherence to coding standards
Understanding of web-based security considerations
Bonus Points:
Experience with token standards (ERC-20, ERC-721, ERC-1155, RWAs) and token list management
Experience with DeFi protocols, token swaps, or fiat on/off ramps
Knowledge of financial products in the digital space
Interest in blockchain technologies and Web3
Basic understanding of cryptography principles
Experience working in distributed teams
Previous experience working in Web3
Familiarity with the Web3 wallet ecosystem
Experience with performance optimization
Other skills to demonstrate:
Good verbal and written communication in English
Teamwork, flexibility, and organization
Eagerness to learn and grow your technical skills
Ability to balance technical quality with business needs and user experience
Don't meet all the requirements? Don't sweat it. We’re passionate about building a diverse team of humans and as such, if you think you've got what it takes for our chaotic-but-fun, remote-friendly, start-up environment—apply anyway, detailing your relevant transferable skills in your cover letter. While we have a pretty good idea of what we need, we're ready for you to challenge our thinking on who needs to be in this role.
The salary range listed for this role applies to US-based candidates only. Compensation for candidates based outside the US (including Canada, EMEA, and LATAM) will be determined based on location, experience, and skills during the interview process, and may differ from the listed US range.
US pay range (not including bonus, equity or other benefits)
$139,000
—
$210,000 USD
In the rapidly evolving Web3 space, we believe that everyone is a builder. This expansive paradigm requires a range of backgrounds, talents, skills, and experiences to influence and shape the future. At MetaMask, this diversity fuels our ability to shift control and redefine the realm of possibility. We are committed to ensuring that our technology empowers people and communities through decentralized technologies. We welcome the range of perspectives and differences and celebrate them. We're excited to see how your unique skills as a builder can contribute to our vision, drive innovation, and help us shape a more inclusive Web3.
MetaMask is an equal opportunity employer. All employment decisions are made without regard to race, color, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, genetic information, religion, disability, medical condition, pregnancy, marital status, family status, veteran status, or any other characteristic protected by law.
MetaMask is aware of fraudulent recruitment practices and we encourage all applicants to review our best practices to protect yourself, which can be found on our page.
MetaMask may use artificial intelligence (AI) tools to support parts of our recruitment process, such as reviewing applications, screening resumes, or conducting initial candidate assessments. These tools are designed to assist our recruiting team and improve efficiency — they do not replace human judgment. A human recruiter or hiring manager reviews every candidate, and all final hiring decisions are made by people, not AI.
It is a requirement of employment in this position that applicants may be required to submit to background and identity verification checks, including but not limited to employment, education, criminal record checks, and other such checks as determined necessary by the company.
Location: Singapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania
Workplace: Remote
Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones)
Start Date: ASAP
Languages: English
Team: Engineering
ABOUT THE ROLE
Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand.
We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities.
This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents.
This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring.
WHAT YOU'LL DO
- Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains.
- Design synthetic task-generation methods that produce diverse, realistic and learnable tasks.
- Build systems and tooling to mutate, validate and improve synthetic tasks.
- Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail.
- Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities.
- Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed.
WHAT WE'RE LOOKING FOR
- Proficiency in Python, Docker and Linux environments.
- Experience with synthetic data research methods — please be ready to describe this in your application.
- A strong understanding of what “good synthetic data” means, and of its limitations.
- Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.
- Experience working on environments, evals or benchmarks.
- Clear written and spoken English, and comfort collaborating remotely across time zones.
- Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments.
- Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.
WHY THIS ROLE MATTERS FOR YOU
- Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline.
- Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents.
- See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail.
- High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics.
- San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.
Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.
Location: United States; Colombia; Sacramento; Los Angeles; San Francisco; Canada; Costa Rica; Mexico
Workplace: Remote
Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones)
Start Date: ASAP
Languages: English
Team: Engineering
ABOUT THE ROLE
Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand.
We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities.
This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents.
This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring.
WHAT YOU'LL DO
- Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains.
- Design synthetic task-generation methods that produce diverse, realistic and learnable tasks.
- Build systems and tooling to mutate, validate and improve synthetic tasks.
- Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail.
- Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities.
- Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed.
WHAT WE'RE LOOKING FOR
- Proficiency in Python, Docker and Linux environments.
- Experience with synthetic data research methods — please be ready to describe this in your application.
- A strong understanding of what “good synthetic data” means, and of its limitations.
- Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap.
- Experience working on environments, evals or benchmarks.
- Clear written and spoken English, and comfort collaborating remotely across time zones.
- Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments.
- Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.
WHY THIS ROLE MATTERS FOR YOU
- Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline.
- Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents.
- See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail.
- High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics.
- San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.
Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.
Location: Singapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania
Workplace: Remote
Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones)
Start Date: ASAP
Languages: English
Team: Engineering
ABOUT THE ROLE
Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand.
We are looking for a Research Engineer, Privacy and Anonymization to build the systems that make sensitive, real-world data safe and still useful for AI training. You will develop methods to detect and remove personally identifiable information, secrets and other sensitive content from raw data before it enters processing and synthetic data pipelines — protecting privacy without destroying the structure and signal that make the data valuable for training agents.
This is a privacy and data-protection engineering seat, not a synthetic-task design role. You will own the pipeline that stands in front of downstream processing, training, evaluation and synthetic generation.
This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring.
WHAT YOU'LL DO
- Build systems to detect PII, quasi-identifiers, credentials and other sensitive information, and design transformations based on the data type and the downstream use case.
- Develop and benchmark detection approaches that combine rules, statistical models, classifiers and LLM-based methods.
- Build production pipelines that anonymize raw data before it enters downstream processing, training, evaluation or synthetic data generation.
- Create evaluation frameworks that measure privacy risk and retained data utility, including recall-weighted metrics, leakage tests and adversarial re-identification attempts.
- Design systems that stay robust to new data sources, schema drift, unusual formats and sensitive information embedded in unexpected fields.
- Work with engineering, research, operations and customers to translate privacy requirements into practical technical policies and safeguards.
WHAT WE'RE LOOKING FOR
- Strong proficiency in Python, and experience building reliable production data or machine-learning systems.
- Experience with information extraction, named-entity recognition, classification, or related methods for detecting rare or sensitive content.
- Strong experimental instincts, and the ability to compare approaches across recall, precision, latency, cost and downstream data utility.
- An understanding of the difference between redaction, masking, pseudonymization, anonymization and synthetic data — and when each is appropriate.
- High attention to detail, and the ability to reason about subtle leakage paths, edge cases and adversarial failure modes.
- Experience building data-processing pipelines end to end, including where there was no fully prescribed roadmap.
- Clear written and spoken English, and comfort collaborating across teams and time zones.
- Strong candidates may also have hands-on experience with privacy-enhancing technologies (such as differential privacy, k-anonymity, secure aggregation or format-preserving encryption); have worked with sensitive data in healthcare, finance or security; have built low-latency or high-throughput ML inference or data-processing systems; or have taken work from early research through to production.
- Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.
WHY THIS ROLE MATTERS FOR YOU
- Make real-world data safe to train on: You will build the systems that let sensitive data be used for AI training without exposing people or secrets.
- Privacy without wrecking utility: The job is not only to redact, but to keep the structure and signal that downstream training and synthetic pipelines need.
- From detection to production: You will own the full path: detection methods, transformations, evaluation, and the pipeline that sits in front of training and generation.
- Hard, high-stakes edge cases: Work on leakage paths, schema drift and adversarial failure modes, not only clean benchmark datasets.
- San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.
Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.
Location: San Juan, Puerto Rico; New York, NY; District of Columbia; Europe | Spain
Workplace: Hybrid
About the Company
Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world.
You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Norwest, Galaxy Ventures, Dragonfly, Bessemer Venture Partners, FirstMark, Lightspeed, and Endeavor Catalyst also participating. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk.
Our Ethos
We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company.
About the role
Rain builds payments and stablecoin infrastructure. Finance, Risk, Compliance, and our partners rely on data from card transactions, settlements, ledger entries, and onchain transfers.
We’re looking for a Data Engineer with batch and streaming experience to bring this data into our warehouse and keep it correct. You’ll build scalable, idempotent pipelines that handle late and duplicate data and alert us when something breaks.
At Rain, everyone models data, writes production-quality code, and automates their own workflows. We use AI to speed up repetitive work, but we own and check the results. In this role, you’ll automate ingestion and quality checks so new sources come online quickly and stay trustworthy.
What you’ll do
- Build scalable batch and streaming pipelines, choosing the right approach for latency, cost, and correctness.
- Process events near real time, handling deduplication, windowing, and late or out-of-order events.
- Own batch ingestion from databases (CDC), files (SFTP and GCS), APIs, and onchain sources.
- Build incremental dbt models that handle late data, duplicates, CDC deletes, and backfills; reconcile streaming and batch outputs.
- Orchestrate pipelines in Dagster with sensors, schedules, and alerts.
- Add freshness, volume, and reconciliation checks to every source, and fix root causes.
- Work with backend pods on event and table contracts to prevent downstream breakage.
- Partner with Analytics Engineers, Data Scientists, and Risk on their data needs, including ML features.
What you’ll need
- At least 3 years building production data solutions, including owning both batch and streaming pipelines end to end, from source to warehouse.
- Experience designing data solutions at scale.
- Strong SQL and Python / Rust, plus experience with a cloud data warehouse such as BigQuery, ClickHouse, or Snowflake and an understanding of their trade-offs.
- Streaming experience with tools such as Pub/Sub, Kafka, Dataflow/Beam, or Flink.
- Batch experience with dbt and an orchestrator such as Dagster or Airflow.
- Experience with production data quality testing, monitoring, and reconciliation.
- Ability to debug across the source database, pipeline, warehouse, and report.
- Ability to use AI tools to learn and work faster while checking their output.
Nice to have
- Building offline and real-time ML feature pipelines, such as fraud or risk features.
- Working with payments, banking, or blockchain data, including EVM or Solana events and indexers.
- Using CDC and ingestion tools such as Datastream, Debezium, Meltano, Fivetran, or Airbyte.
- Experience with GCP.
Things that enable a fulfilling, healthy and happy experience at Rain:
Flexible time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves.
Flexible working ☕ We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves. New Rainmakers receive a stipend to set up a comfortable home workspace.
Easy to access benefits 🧠 For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan.
Retirement goals💡 Plan for the future with confidence. We offer a 401(k) with a 4% company match.
Equity plan 📦 Every Rainmaker gets an equity option plan so we all benefit from our success.
Health and Wellness 📚 High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you!
In-office meals 🍜 Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit.
Team summits ✨ Summits play an important role at Rain. Time together helps us build relationships and a common destiny. Expect team and company offsites, both domestic and international.
seniorGermany (Remote)
seniorSpain (Remote)
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
seniorПротиворечивые условия: Please note that we are unable to consider applications from candidates based in France, Italy, or Germany for this role; Compensation for candidates based outside the US (including Canada, EMEA, and LATAM) will be determined based on location, experience, and skills during the interview process, and may differ from the listed US range
УдалённоSingapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania; Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones); Clear written and spoken English, and comfort collaborating remotely across time zones
УдалённоUnited States; Colombia; Sacramento; Los Angeles; San Francisco; Canada; Costa Rica; Mexico; Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones); Clear written and spoken English, and comfort collaborating remotely across time zones
seniorUnited States - Remote
УдалённоSingapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania; Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones); Clear written and spoken English, and comfort collaborating across teams and time zones
ГибридSan Juan, Puerto Rico; New York, NY; District of Columbia; Europe | Spain
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