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Location: Bengaluru, India We are looking for a Senior Data QE Engineer with solid experience in ETL testing and data validation, and strong SQL skills. You will design and run data quality tests, validate pipelines end to end, reconcile source and analytical data, and give engineering and business teams evidence for release decisions. You will also automate comparisons in Python, work with Airflow pipeline runs, and use AWS and Grafana to observe pipeline health. You will use an AI coding assistant to analyze data and author SQL, Python, and quality checks, and you will review generated output before it is used. This position is intended for Hyderabad-based candidates who are available to work from the office. The client runs a large application landscape and the analytical datasets behind it. Data moves from PostgreSQL sources on AWS into Snowflake through orchestrated pipelines. Quality for that data is part of delivery: test design, checks on movement and transformation, quality metrics, and practical help when data has to be corrected. Data QE engineers own that hands-on work for data moving from source systems into analytical datasets, from test design through execution, investigation, and release-readiness reporting. Own data quality for data moving from source systems into analytical datasets, from test design through execution, investigation, and reporting. Design, implement, and execute data quality tests, and publish quality metrics and dashboards that producing and consuming groups can use. Validate pipelines end to end, covering movement, transformation, completeness, history, freshness, and compliance. Write reconciliation, drift, and quality SQL on Snowflake layered models (raw, history, silver, and gold), including slowly changing dimensions and merge behavior. Validate PostgreSQL sources with consistent snapshot counts and checks that logical replication kept the expected data. Use logical and physical data models and data contracts to assess downstream impact and set quality coverage. Automate data comparison and reconciliation in Python with pytest, and keep the checks in GitHub. Trigger pipeline runs in Airflow and read task results into the quality verdict. Investigate quality issues and support remediation with the groups that produce and consume the data. Design failover, outage, and volume tests and judge the evidence, including recovery time and recovery point. The platform team executes destructive and chaos tests. Run read-only checks in pre-production and production. Integrate quality checks with CI/CD on GitHub Actions or an equivalent tool. Use AWS and Grafana to observe pipeline health, including CloudWatch metrics, secrets metadata, and container platform state. Check classification, masking, and retention against the stated data rules. Track defects to closure in Jira and keep test cases and results in TestRail. Provide quality evidence, risks, and readiness input for releases. Use an AI coding assistant to analyze data and author SQL, Python, and quality checks, and review generated output before it is used. Senior-level data quality engineering experience in ETL testing and data validation. Strong SQL and Snowflake data-modelling experience, including layered raw, history, silver, and gold models, reconciliation and drift queries, and slowly changing dimensions and merge behavior. Experience with logical and physical data models and with assessing the downstream impact of schema and data-contract changes. Experience validating PostgreSQL sources, including consistent snapshot counts and logical replication. Hands-on Python automation for data comparison and reconciliation, using pytest or an equivalent framework. Experience running and reading orchestrated pipelines in Airflow or an equivalent scheduler. Experience validating data movement, transformation, completeness, history, freshness, and compliance from source systems into analytical datasets. Experience diagnosing data quality issues and supporting remediation. Experience integrating data quality checks with CI/CD using GitHub Actions or equivalent tools. Experience with test management and defect tracking in TestRail and Jira. Working knowledge of AWS and Grafana for pipeline observation, including CloudWatch, secrets management, and container platforms. Experience designing resilience tests and judging evidence for failover, outage, volume, and recovery objectives. Working knowledge of data classification, masking, and retention checks. Experience producing data quality metrics, reports, and dashboards. Experience with risk-based testing in Agile or Kanban teams. Hands-on use of AI coding assistants (Cursor, GitHub Copilot, Claude Code, Codex, or equivalent) to author SQL, Python, and quality checks, with human review of generated output. Ability to communicate clearly with engineering and business stakeholders. Experience providing quality evidence, risks, and readiness input for releases. Experience observing Snowflake Openflow or NiFi connector state well enough to recognize a stalled load. Experience with custom cross-table data quality checks that publish results for dashboarding. Familiarity with GenAI, LLMs, and agentic workflows for maintaining data quality checks. Familiarity with RAG and MCP concepts for QE tooling and for generating test data. Knowledge of AI-driven test optimization and coverage analytics (SeaLights or equivalent). Experience with service virtualization using WireMock or an equivalent tool, so checks can run when a dependency is unavailable. Experience specifying and reviewing dbt tests. Developers author those tests.

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Team Description The Privacy and Compliance Engineering team is responsible for preparing Reddit’s platforms for evolving regulatory requirements. We’re building products that establish the foundation of trust in the Reddit platform, while partnering closely with teams across the company to ensure our platforms and features meet the highest standards. Engineers on this team will thrive in dynamic, ambiguous problem spaces and work across multiple domains and technology stacks. Role Description We’re hiring a GRC Software Engineer to build and improve the software that supports Reddit’s security and compliance programs. You will design, implement, and maintain integrations, data pipelines, scheduled workflows, and automated checks across cloud, identity, HR, finance, code, and ticketing systems. Working closely with engineering and business partners, you’ll translate requirements into reliable tools that surface issues early and reduce manual work. You’ll own projects from discovery through launch, make clear technical trade-offs, and establish sustainable support with engineering partners. How You’ll Have Impact: This role will make Reddit’s compliance programs more effective and scalable by building systems that help teams address requirements early and automate recurring assurance work. You’ll work with engineers and control owners to define clear requirements, deliver practical solutions, and follow through after launch. Your work will improve how controls operate, make evidence more timely and reliable, and surface gaps sooner. Success means dependable systems that teams continue to use, less manual work, faster remediation, and clearer visibility into risk. What You’ll Do: Own software projects from discovery through launch: work with partners to define the problem, agree on success measures, make technical trade-offs clear, deliver the solution, and follow through on results. Lead efforts to automate evidence collection across key systems in scope for SOX and other compliance requirements. Work with control owners and system teams to define evidence requirements, build and validate integrations, monitor failures, and establish ongoing support. Build and maintain integrations and scheduled workflows across cloud, identity, HR, finance, ticketing, version control, CI/CD, and GRC systems. Use APIs, scripts, queries, and orchestration tools to collect, normalize, validate, and retain data from authoritative systems. Own authentication, data modeling, testing, monitoring, failure recovery, documentation, and maintenance. Design data models that connect risks, controls, assets, owners, systems, and evidence across sources with different schemas, identifiers, permissions, and data quality. Build automated checks that detect missing, stale, inconsistent, or out-of-policy data, and route actionable failures to the right owners. Turn control requirements and audit requests into data specifications, testable checks, and exception logic. Partner with control owners to validate system scope and resolve gaps. Build strong working relationships across Security, Engineering, Infrastructure, Product, Privacy, Legal, and Finance. Learn how their systems operate and deliver tools, monitoring, and runbooks they can rely on after launch. Role Requirements: 7+ years of experience in security, compliance, automation, data integration, security engineering, or a related technical field. Hands-on proficiency with Python, Go, or a comparable language and SQL, including writing, testing, reviewing, and maintaining code used by other people or operational workflows. Experience building API-based integrations using REST APIs, webhooks or polling, authentication flows, pagination, rate limits, error handling, and structured data formats. Experience building scheduled data workflows with an orchestrator such as Airflow and moving validated data between enterprise SaaS systems, databases, and cloud object storage. Technical fluency with a major cloud platform and its identity, storage, logging, monitoring, and secrets-management services; GCP experience is helpful but not required. Experience with version control, automated testing, code review, CI/CD, observability, secure credential handling, failure recovery, and the runbooks needed to support an integration after launch. Practical knowledge of security and compliance controls, including how requirements translate into system design, evidence, testing, and remediation. Experience applying one or more relevant frameworks or regulatory requirements, such as SOC 2, ISO 27001, SOX ITGC, NIST CSF, EU DSA, or EU NIS 2. Demonstrated ability to earn trust and influence without authority by listening carefully, making risk and technical trade-offs clear, reducing partner burden where possible, addressing concerns directly, and following through on commitments. You can independently own complex projects from discovery through launch and measurable results. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $190,800 — $267,100 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Задачи: - Работа с SQL и Python - Моделирование данных (DWH / Data Lake / Lake House) - Работа с ClickHouse, Redshift, BigQuery - Работа с Big Data стеком (HDFS, Spark, Cloud, Object Storage) - Работа с ETL / ELT инструментами (Airflow, Flink, Dagster) - Использование Agile методологий - Интеграция с AI агентами (Cloude Code, ChatGPT, Copilot, OpenCode) Требования: - Коммерческий опыт работы от 2 лет - Знание SQL и Python - Английский язык на уровне B2 и выше Будет плюсом: - Опыт работы с веб-фреймворками (Flask, FastAPI, Django) - Опыт работы с системами обмена сообщениями (Kafka, RabbitMQ, Kinesis, SQS) - Опыт работы с облачными хранилищами (AWS, Azure, GCP) - Опыт работы с CI/CD Условия: - Просторный офис А класса в нескольких шагах от метро - Защищённая от инфляции заработная плата - Гибкий рабочий график - Молодой коллектив, ценящий инновации и совместную работу Вакансия перспективная, планируемая к созданию и/или замещению.

Location: United States (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 are looking for candidates from the U.S. The Opportunity Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. You’ll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You’ll ship production systems that teams depend on daily. This is a high-autonomy role where you own the technical direction. You’ll identify the highest-leverage problems across Marketing, RevOps, and SDR teams, design the solutions, and ship them. You’ll define the technical direction for the automation platform (data models, API contracts, shared libraries, reference architectures) and partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency. What You'll Be Doing Agentic Systems & AI Infrastructure Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs) Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths Systems Integration & Backend Services Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools) Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure Automation & Workflow Enablement Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards. What Makes You a Great Fit 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring You diagnose business problems before writing code. You think in workflows and outcomes, not just functions. Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run) Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders Bonus Points Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector) Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach) Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks) Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow) Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment Active in open-source communities. Grafana is built on OSS and we value engineers who share that DNA In the United States, the base compensation range for this role is USD $154,445 - USD $185,334. 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: Canada (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 are looking for candidates from Canada. Residents of Quebec are not eligible for this role. The Opportunity Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. You’ll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You’ll ship production systems that teams depend on daily. This is a high-autonomy role where you own the technical direction. You’ll identify the highest-leverage problems across Marketing, RevOps, and SDR teams, design the solutions, and ship them. You’ll define the technical direction for the automation platform (data models, API contracts, shared libraries, reference architectures) and partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency. What You'll Be Doing Agentic Systems & AI Infrastructure Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs) Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths Systems Integration & Backend Services Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools) Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure Automation & Workflow Enablement Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards. What Makes You a Great Fit 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring You diagnose business problems before writing code. You think in workflows and outcomes, not just functions. Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run) Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders Bonus Points Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector) Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach) Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks) Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow) Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment Active in open-source communities. Grafana is built on OSS and we value engineers who share that DNA In Canada, the base compensation range for this role is CAD 164,490 - CAD 197,389 . 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: Argentina Workplace: Remote Webflow is the agentic web marketing platform for modern marketing teams, helping organizations build, manage, and optimize high-performing web experiences that drive predictable growth and strengthen brand trust. Building at Webflow will need grit, because we move fast, without ever sacrificing craft or quality. We’re looking for a Senior Infrastructure/SRE Engineer to improve the reliability and stability of the customer-facing production infrastructure behind Webflow’s Convert pillar. Convert owns a suite of products that give marketing and growth teams the tools to experiment, personalize, and drive real revenue from their web presence, including Webflow Analyze, Optimize, AEO, and Campaigns. As tens of thousands of projects launch on Webflow each month, you’ll help keep the platform secure, scalable, and dependable for the people who rely on it. Much of what Convert delivers depends on data: high-volume event ingestion, real-time and analytical data stores, and pipelines that turn visitor behavior into reporting, experiments, and personalization. You’ll own the reliability, performance, and cost of this data infrastructure, including streaming and batch pipelines and analytical databases. You’ll set SLOs, build observability, plan capacity, and make sure data stays fresh, accurate, and available as volume grows. The next chapter is ambitious. We’re serving some of the world’s largest enterprises and rethinking what an AI-native conversion platform can be. You’ll work across a large, revenue-generating system, partner with the engineers who build on it, and help decide how it needs to evolve.   About the role: - Location: Remote-first (Argentina) - Permanent, full-time employee (this is not a contractor position) - Application Information: - Application deadline: applications accepted on an ongoing basis until position is closed and filled - This posting is for a new position. As a Senior Infrastructure/SRE Engineer on the Convert pillar, you'll: - Own and evolve the cloud infrastructure behind the Convert products, including compute, serverless, networking, and cloud operations on AWS. - Build and maintain our infrastructure-as-code foundation in Pulumi, and evolve the patterns and shared components other teams build on. - Improve scalability and reliability of distributed, event-driven systems, including our Druid, MongoDB, and Airflow workloads, with a focus on low latency. - Make observability the foundation of how we operate. Improve the dashboards, alerts, and SLOs for the systems you own so we catch issues before customers notice, and on-call pages are actionable instead of noisy. - Lead projects from design through launch that move team and engineering org goals. - Write and review maintainable, tested, performant code and automation, and raise the quality bar for the team. - Build AI-powered automation, from policy-as-code and drift detection to LLM-assisted runbook generation, and use AI tools in your daily work. - Help the team build reliable systems. ABOUT YOU Requirements: - BA/BS degree or equivalent experience - Business-level fluency to read, write and speak in English You’ll thrive as a Senior Infrastructure/SRE Engineer if you: - Have 5+ years owning and operating cloud infrastructure in a customer-facing environment with little tolerance for downtime. You come from infrastructure, SRE, or cloud engineering with a love of automation and code, or from software engineering with a deep interest in cloud infrastructure and distributed systems. - Have deep hands-on experience with AWS and a clear view of what good cloud operations look like. - Have used infrastructure as code (Pulumi or Terraform) and prefer changes made through code, not consoles. - Have operated high-throughput, data-intensive distributed systems in production, such as Druid, MongoDB, or Airflow, and understand scalability, fault tolerance, consistency trade-offs, and performance tuning. - Are fluent in Python or TypeScript/Node.js for automation and tooling, and can read and work in Java services. - Treat observability as the basis of operations: you design SLOs, dashboards, and alerts so that issues surface before customers notice and pages are actionable. - Handle incidents well, including on-call and blameless postmortems, and fix the root causes behind them instead of just closing tickets. - Want to improve developer productivity, with CI/CD, tooling, and paved paths that let product engineers ship without owning infrastructure. It would be a bonus if you had even one of the following: - Experience with Apache Spark, Airflow, or TensorFlow. - Familiarity with Kafka or other event streaming platforms. - Experience building AI-assisted infrastructure tooling, including cost optimization loops, anomaly detection, or policy-as-code with LLM assistance. - Experience contributing to multi-region architecture including data residency, regional failover, or latency-based routing. OUR CORE BEHAVIORS - Build lasting customer trust. We build trust by taking action that puts customer trust first. - Win together. We play to win, and we win as one team. Success at Webflow isn't a solo act. - Reinvent ourselves. We don't just improve what exists, we imagine what's possible. - Own it all the way. We move fast because the moment demands it, and we hold ourselves accountable to deliver outcomes — for each other, and for customers.   BENEFITS - Ownership in what you help build. Every permanent Webflower receives equity (RSUs) in our growing, privately held company. - Health coverage that actually covers you. Comprehensive medical, dental, and vision plans for full-time employees and their dependents, with Webflow covering most premiums. - Support for every stage of family life. 12 weeks of paid parental leave for all parents and 6+ weeks of additional paid leave for birthing parents. Plus inclusive care for family planning, menopause, and midlife transitions. - Time off that’s actually off. Flexible vacation, paid holidays, and a sabbatical program to help you recharge and come back inspired. - Wellness for the whole you. Access to mental health resources, therapy and coaching. - Invest in your future. A 401(k) with 100% employer match (up to $6,000/year) in the U.S., and support for retirement savings globally. - Stipends to support our distributed team. Localized support for cell, Wi-Fi, and WFH expenses to help you settle into your best remote life. - Bonus for building together. All full-time, permanent, non-commission employees are eligible for our annual WIN bonus program.   Temporary employees may be eligible for paid holiday and time off, statutory leaves of absence, and company-sponsored medical benefits depending on their Fixed Term Contract and their country/state of employment.   BE YOU, WITH US At Webflow, equality is a core tenet of our culture. We are an Equal Opportunity (EEO)/Veterans/Disabled Employer and are committed https://webflow.com/diversity-equity-inclusion to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences. Employment decisions are made on the basis of job-related criteria without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other classification protected by applicable law. Pursuant to the San Francisco Fair Chance Ordinance, Webflow will consider for employment qualified applicants with arrest and conviction records.   STAY CONNECTED Not ready to apply, but want to be part of the Webflow community? Consider following our story on our Webflow Blog https://webflow.com/blog, LinkedIn https://www.linkedin.com/company/webflow-inc-/, X (Twitter) https://twitter.com/webflow, and/or Glassdoor https://www.glassdoor.com/Reviews/Webflow-Reviews-E890506.htm.   PLEASE NOTE: We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Upon interview scheduling, instructions for accommodation requests will be administered. To join Webflow, you'll need a valid right to work authorization depending on the country of employment. If you are extended an offer, that offer may be contingent upon your successful completion of a background check, which will be conducted in accordance with applicable laws. We may obtain one or more background screening reports about you, solely for employment purposes. Webflow uses AI tools to assist in screening and evaluating applications. AI does not make final hiring decisions. For details on how we process your personal information, see Webflow’s Applicant Privacy Notice https://webflow.com/legal/applicant-privacy-notice.

Задачи: — Руководство командой инженеров данных — Координация с аналитикой и продуктовыми командами — Участие в проектировании архитектуры новых проектов — Поддержка и расширение существующих систем — Оптимизация и автоматизация рабочих процессов — Проведение командных ритуалов (планирование, стендапы, Planning Poker) Требования: — Опыт руководства командой 3–5 человек либо работы в роли техлида с ответственностью за сроки — Опыт кросс‑функциональных проектов (инженерия, аналитика, продукт) — Навыки оценки сроков и передачи знаний, документация и конструктивная коммуникация — Технические навыки: Apache Airflow, Apache Spark, ClickHouse, dbt, работа с объектным хранилищем/data lake (S3, Apache Iceberg — плюс), Kafka, Python и SQL на уровне продакшена — Понимание продуктовой аналитики и атрибуции (AppsFlyer, MyTracker, трекеры рекламных сетей — плюс) — Будет плюсом опыт в ad‑tech, мобильной/маркетинговой аналитике, миграциях хранилищ, использовании AI‑инструментов в data‑инжиниринге.

• Разрабатывать и поддерживать ETL/ELT-процессы и витрины данных. • Интегрировать данные из баз данных, API и файлов.

Location: European Union; Ukraine Workplace: Remote ABOUT US Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/ ABOUT THE ROLE We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization. You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse). This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting. You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered. Responsibilities - Design, build, and maintain dbt models (staging, core, marts) based on business needs - Develop scalable SQL transformations and ensure efficient, cost-effective query performance - Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability. - Create and maintain documentation for datasets, data lineage, business logic, and transformation rules - Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness - Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models - Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting - Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization - Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making Requirements - Strong SQL experience with a deep understanding of analytical modeling concepts - Hands-on experience with dbt (models, tests, docs, macros) - Experience working with cloud data warehouses such as BigQuery or ClickHouse - Understanding of data modeling patterns - Familiarity with data engineering concepts (ELT/ETL, orchestration, version control, CI/CD) - Experience with data quality frameworks or implementing validation logic - Ability to translate complex business processes into clean analytical datasets. - Strong documentation skills and attention to detail - Experience collaborating with cross-functional teams and supporting analysts with reliable data - Knowledge of Python is a plus (but not required) Nice To Haves - Experience with orchestration tools (Airflow, Dagster) - Experience working with marketing, product, or billing datasets - Exposure to metric layers, semantic layer design, or BI governance - Basic understanding of observability tooling (Grafana, Prometheus, ... ) - Experience optimizing BigQuery costs or ClickHouse performance LOCATION Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. BENEFITS Discover the perks of being part of our vibrant team! We offer: - Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. - Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. - Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. - Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. - Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! INTERVIEW PROCESS After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: - Recruiter Screening (40 minutes) - Technical Interview (90 minutes) - Final Interview (60 minutes) LIFE AT RUBY LABS At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote

Location: Алматы Задачи Разрабатывать ML модели для широкого круга задач; Участвовать в разработке сервиса на основе ML на всех этапах: от сбора данных до выкатки в продакшн и дальнейшего мониторинга/сопровождения сервиса; При необходимости оптимизировать модели под HighLoad системы; Тесное взаимодействие со своей продуктовой командой; Работать с нейронными сетями и обучать PyTorch модели для production; Интегрировать языковые модели (LLM/SLM) в существующие сервисы компании. Требования Понимание теории вероятности, математической статистики, линейной алгебры; Знание “классического ML” на хорошем уровне (регрессия, методы кластеризации, деревья решений и их ансамбли); Знание основ Deep Learning в сфере CV и NLP (работа с разнородными данными: изображения, текст, табулярные данные); Владение Python (опыт использования Pytorch) и связанным с ним стэком для ML (pandas, sklearn, numpy); Знание Git и Docker. Будет плюсом Опыт разработки ML решений в одной из следующих областей: NLP, CV, Recommendation Systems; Опыт работы с DVC, MLFlow; Опыт работы с airflow, bigquery; Понимание kubernetes.

-Уверенное понимание принципов построения дата-платформ (DWH, OLAP, Data Lake, Lakehouse). -Знание методологий проектирования модели данных (Kimball, Data Vault), реальный опыт применения их на практике для проектирования моделей данных.

Задачи: — построение ETL-процессов: разработка и настройка конвейеров по сбору и трансформации данных из различных источников; — работа с современным стеком трансформации: освоение и применение DBT для превращения «сырых» данных в чистые аналитические витрины; — оркестрация данных: настройка и поддержка рабочих процессов (pipelines) в Dagster для своевременного и безошибочного обновления данных в отчетах; — управление хранилищами: работа с Postgres и высокопроизводительной колончатой базой данных Clickhouse для обеспечения скорости работы отчетов; — визуализация смыслов: разработка интерактивных BI-отчетов в Apache Superset для поддержки принятия бизнес-решений; — оптимизация выборок: анализ и ускорение тяжелых запросов для повышения производительности системы отчетности. Требования: — уверенное владение SQL (сложные выборки, агрегатные функции, оптимизация запросов); — понимание устройства реляционных баз данных, в частности PostgreSQL, и принципов построения аналитических хранилищ; — базовые знания Python, умение писать скрипты для обработки данных и автоматизации простых задач; — аналитический склад ума, способность работать с большими массивами данных, выявлять закономерности и структурировать информацию. Желательный опыт: — создание дашбордов в BI-инструментах (Apache Superset, Tableau, Power BI или аналогичных); — понимание концепций ETL/ELT; — понимание архитектуры данных, разницы между OLTP и OLAP системами (например, использование Clickhouse для аналитики); — интерес к современным инструментам DBT и оркестраторам данных (Airflow, Dagster). Условия: — срочный трудовой договор на 3 месяца; — график 5/2, не более 30 часов в неделю; — возможность работы удаленно, в офисе или в гибридном формате; — стажировка открыта для студентов любых ВУЗов и форм обучения, включая заочную и магистратуру.

Задачи: — Проектировать и поддерживать интеграцию источников данных (взаимодействие с процессинговыми системами и Автоматизированной Банковской Системой / АБС); — Организовывать мониторинг актуальности входящих данных и оперативно реагировать на инциденты; — Готовить данные для загрузки в оговоренные форматы хранения (Data Lake / DWH) для последующего потребления смежными командами; — Разрабатывать и оптимизировать витрины данных для BI-отчетности в Superset и PowerBI; — Проводить глубокую проверку качества данных (Data Quality), выявлять аномалии и устранять корневые причины ошибок; — Обеспечивать бесперебойную актуальность данных, нести ответственность за соблюдение SLA и быстрое восстановление при авариях. Требования: — Опыт в направлении финтех; — Отличное знание SQL (DDL/DML) в PostgreSQL; — Хорошее знание SQL (DDL/DML) в ClickHouse; — Отличные навыки оптимизации SQL-запросов в PostgreSQL; — Отличные навыки работы с Python: написание и поддержка DAG-ов в Airflow, опыт работы с асинхронными сервисами; — Хорошие навыки конфигурирования PostgreSQL.

Location: Москва Мы помогаем государству проводить disrupt госуправления через внедрение AI/GenAI и ускорять национальное развитие AI/GenAI, организуем одно из крупнейших в мире профильных мероприятий – AIJourney, объединяем бизнес, государство и международное научное сообщество, чтобы обеспечить GenAI-трансформацию страны. Обязанности • cтроить и развивать систему оценки качества LLM, RAG и агентов: тестовые наборы, метрики, автоматические прогоны, отчёты • проводить регрессионное тестирование в CI при смене моделей, промптов и настроек, контролировать качество перед релизами и показами • оценивать агентов по траекториям: успешность задач, ошибки вызова инструментов, лишние шаги • калибровать автоматическую оценку (LLM-as-a-judge) против экспертной разметки • строить пайплайны обработки документов: PDF, сканы, OCR, таблицы, восстановление структуры • готовить датасеты для дообучения, включая синтетические данные и траектории вызова инструментов. Требования • от 3 лет в Data Science, Data Engineering или ML, из них не менее года с LLM-проектами • опыт построения с нуля системы оценки качества LLM или RAG • знание метрик поиска (recall@k, MRR, nDCG) и метрик ответов (faithfulness, полнота, корректность) • опыт оценки агентных систем или многошаговых сценариев • прикладная статистика: доверительные интервалы, значимость различий, размер выборки • опыт обработки реальных документов в продакшене, включая OCR • Python на уровне production-кода, уверенный SQL • оркестрация пайплайнов (Airflow, Prefect, Dagster или аналоги), версионирование данных и трекинг экспериментов (DVC, MLflow или аналоги). **Будет плюсом:** • работа с юридическими, нормативными или канцелярскими текстами на русском языке • классический NLP: NER, классификация, извлечение сущностей • опыт с русскоязычными бенчмарками • работа с персональными данными по 152-ФЗ • Elasticsearch, OpenSearch, векторные базы данных. Условия * гибридный формат работы (м Кутузовская) * ежегодный пересмотр зарплаты, годовой бонус * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера * корпоративная пенсионная программа.

Location: Москва Workplace: Гибрид Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Обязанности Hi-Level-анализ требований, проработка и согласование требований с заказчиком; Разработка и тестирование BI-дашбордов, отчётов и выгрузок; Контроль качества данных в выгрузках; Сбор информации, сроков и зависимостей от смежных команд; Постановка задач на команду разработки (когда это требуется). Требования Уверенное владение SQL: сложные запросы, оптимизация, работа с большими объёмами данных; Обязательный опыт разработки на Python; Практический опыт построения и поддержки ETL/ELT-процессов и пайплайнов обработки данных; Опыт работы с Apache Airflow будет большим преимуществом; Знакомство с Trino, Iceberg и Spark будет плюсом; Опыт работы с BI-системами и/или регуляторной отчётностью приветствуется. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

• Принимать и закрывать заявки в трекере: доступы, окружения, настройки, типовые вопросы разработки, аналитики и поддержки. • Выдавать, изменять и отзывать доступы по ролевой модели (Keycloak, Vault, GitLab, VPN).