Location: San Francisco; New York City
In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people and teams adopt Perplexity and Computer on their own, and the Growth team owns the loops that turn that adoption into lasting usage and revenue.
We're hiring a Growth GTM Engineer to build the systems that turn product usage into revenue and turn self-serve usage into enterprise pipeline. You'll turn first-party Computer and Enterprise usage into account-level signals. You'll ship the in-product paths that capture sales intent, and build and validate those signals with data science. Alongside product engineers, you ship production code, instrument the product and run experiments. You'll partner closely with Revenue Operations, who own Salesforce, CPQ, routing and sales reporting.
Tech Stack: TypeScript | Next.js | React | Python | PostgreSQL | Snowflake | Eppo | AWS
WHY PERPLEXITY IS DIFFERENT
- Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious.
- Ownership. You identify the problem, design the solution and ship it.
- Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users.
- Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products.
- Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence.
WHAT YOU'LL DO
- Build account-level PLG signals: map users and workspaces to accounts, and define the activation, expansion and intent signals that predict an enterprise deal. Publish them as trusted models that sales use for scoring and routing.
- Own the in-product paths from self-serve to sales, like contact-sales entry points, high-intent upsells and meeting booking, across web and app. Decide who sees them, when and with what context, and measure them end to end, from surface to booked meeting to pipeline.
- Lead experiments end to end on sales-assist surfaces and targeting, from hypothesis and instrumentation through analysis and rollout. Partner with Data Science to measure meetings and qualified opportunities, not just clicks.
- Build the internal tools and automation Growth and GTM teams run on
- Partner closely with Sales, Sales Systems, Data Science, Marketing and Product to turn product signals into pipeline. Be upstream partner to sales systems that own Salesforce, CPQ and routing
QUALIFICATIONS
- 2+ years as a growth, GTM, data or full-stack engineer, shipping production code (TypeScript/React, Python).
- Strong SQL and comfort building data models in a warehouse (Snowflake, dbt-style).
- Experience instrumenting product events and running A/B experiments end to end.
- Commercial instinct: you start from a revenue problem ("trial-to-enterprise conversion won't move") and work back to the signal or surface that fixes it.
- Comfort with REST APIs, webhooks and integrating third-party GTM tools.
- Operator mindset: you talk to customers directly, ship fixes, and don't need a PM to translate requests.
NICE TO HAVE
- Experience in a PLG-to-enterprise motion: product-qualified leads, scoring users into accounts, sales-assist flows.
- Experience as a GTM or growth engineer at a B2B SaaS or GTM-tooling company.
- Entity resolution, dedup or domain enrichment.
- Familiarity with Salesforce, Default, Chili Piper, Intercom, Clay, Hightouch/Polytomic or similar (as a consumer and integrator, not an admin).
- Experience building LLM-powered internal tools or agents.
Location: San Francisco; New York City
In 2026, we launched Computer, the defining product for the new era of agentic AI. Millions of people and teams adopt Perplexity and Computer on their own, and the Growth team owns the loops that turn that adoption into lasting usage and revenue.
We're hiring a Growth GTM Engineer to build the systems that turn product usage into revenue and turn self-serve usage into enterprise pipeline. You'll turn first-party Computer and Enterprise usage into account-level signals. You'll ship the in-product paths that capture sales intent, and build and validate those signals with data science. Alongside product engineers, you ship production code, instrument the product and run experiments. You'll partner closely with Revenue Operations, who own Salesforce, CPQ, routing and sales reporting.
Tech Stack: TypeScript | Next.js | React | Python | PostgreSQL | Snowflake | Eppo | AWS
WHY PERPLEXITY IS DIFFERENT
- Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious.
- Ownership. You identify the problem, design the solution and ship it.
- Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users.
- Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products.
- Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence.
WHAT YOU'LL DO
- Build account-level PLG signals: map users and workspaces to accounts, and define the activation, expansion and intent signals that predict an enterprise deal. Publish them as trusted models that sales use for scoring and routing.
- Own the in-product paths from self-serve to sales, like contact-sales entry points, high-intent upsells and meeting booking, across web and app. Decide who sees them, when and with what context, and measure them end to end, from surface to booked meeting to pipeline.
- Lead experiments end to end on sales-assist surfaces and targeting, from hypothesis and instrumentation through analysis and rollout. Partner with Data Science to measure meetings and qualified opportunities, not just clicks.
- Build the internal tools and automation Growth and GTM teams run on
- Partner closely with Sales, Sales Systems, Data Science, Marketing and Product to turn product signals into pipeline. Be upstream partner to sales systems that own Salesforce, CPQ and routing
QUALIFICATIONS
- 2+ years as a growth, GTM, data or full-stack engineer, shipping production code (TypeScript/React, Python).
- Strong SQL and comfort building data models in a warehouse (Snowflake, dbt-style).
- Experience instrumenting product events and running A/B experiments end to end.
- Commercial instinct: you start from a revenue problem ("trial-to-enterprise conversion won't move") and work back to the signal or surface that fixes it.
- Comfort with REST APIs, webhooks and integrating third-party GTM tools.
- Operator mindset: you talk to customers directly, ship fixes, and don't need a PM to translate requests.
NICE TO HAVE
- Experience in a PLG-to-enterprise motion: product-qualified leads, scoring users into accounts, sales-assist flows.
- Experience as a GTM or growth engineer at a B2B SaaS or GTM-tooling company.
- Entity resolution, dedup or domain enrichment.
- Familiarity with Salesforce, Default, Chili Piper, Intercom, Clay, Hightouch/Polytomic or similar (as a consumer and integrator, not an admin).
- Experience building LLM-powered internal tools or agents.
Location: Canada
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
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: Sapugaskanda, Sri Lanka
Make a Meaningful Impact—From Anywhere.
Join Intellect as a Clinical Provider and support clients on their mental health journeys through our innovative telehealth platform. You’ll deliver high-quality, evidence-based care while being part of a supportive, global network of professionals.
This freelance, remote role offers complete flexibility, allowing you to manage your schedule while complementing your current work or private practice.
Why Join Intellect
You’ll become part of a trusted community of Clinical Psychologists and Counsellors committed to expanding access to quality mental health care worldwide.
What You’ll Do
Provide 1-on-1 telehealth and/or onsite clinical support
Treat a broad range of cases including anxiety, depression, trauma, addiction, eating disorders, schizophrenia, and more
Partner with our internal clinical team to improve programs and platform outcomes
Requirements
Education: Master’s degree in Counseling or Clinical Psychology
Expertise: CBT or other evidence-based approaches (MI, DBT, SFBT, etc.)
Experience: Minimum of 300 post-master’s clinical hours
Languages: Fluency in English and Sinhala
Preferred: EAP experience, adult counseling, and CISP
Bonus: Openness to occasional onsite work
If you’re passionate about delivering impactful mental health care, we’d love to hear from you. Apply today.
Location: Amman, Jordan
Make a Meaningful Impact—From Anywhere.
Join Intellect as a Clinical Provider and support clients on their mental health journeys through our innovative telehealth platform. You’ll deliver high-quality, evidence-based care while being part of a supportive, global network of professionals.
This freelance, remote role offers complete flexibility, allowing you to manage your schedule while complementing your current work or private practice.
Why Join Intellect
You’ll become part of a trusted community of Clinical Psychologists and Counsellors committed to expanding access to quality mental health care worldwide.
What You’ll Do
Provide 1-on-1 telehealth and/or onsite clinical support
Treat a broad range of cases including anxiety, depression, trauma, addiction, eating disorders, schizophrenia, and more
Partner with our internal clinical team to improve programs and platform outcomes
Requirements
Education: Master’s degree in Counseling or Clinical Psychology
Expertise: CBT or other evidence-based approaches (MI, DBT, SFBT, etc.)
Experience: Minimum of 300 post-master’s clinical hours
Languages: Fluency in English and Arabic
Preferred: EAP experience, adult counseling, and CISP
Bonus: Openness to occasional onsite work
If you’re passionate about delivering impactful mental health care, we’d love to hear from you. Apply today.
Location: West Sumatra, Indonesia
Make a Meaningful Impact—From Anywhere.
Join Intellect as a Clinical Provider and support clients on their mental health journeys through our innovative telehealth platform. You’ll deliver high-quality, evidence-based care while being part of a supportive, global network of professionals.
This freelance, remote role offers complete flexibility, allowing you to manage your schedule while complementing your current work or private practice.
Why Join Intellect
You’ll become part of a trusted community of Clinical Psychologists and Counsellors committed to expanding access to quality mental health care worldwide.
What You’ll Do
Provide 1-on-1 telehealth and/or onsite clinical support
Treat a broad range of cases including anxiety, depression, trauma, addiction, eating disorders, schizophrenia, and more
Partner with our internal clinical team to improve programs and platform outcomes
Requirements
Education:
Master’s degree in
Counseling
or
Clinical Psychology
Expertise:
CBT or other evidence-based approaches (MI, DBT, SFBT, etc.)
License:
Must hold valid and active
SIPP (Surat Izin Praktik Psikolog)
or SIPPK (Surat Iz
in Praktik Psikolog Klinis)
issued in Indonesia, along with a
current registration (STRP).
Experience:
Minimum of
300 post-master’s clinical hours
Languages:
Fluency in
English
and
Bahasa Indonesia
Preferred:
EAP experience, adult counseling, or managerial coaching
Bonus:
Openness to occasional onsite work in
Sei Mangkei, North Sumatra
If you’re passionate about delivering impactful mental health care, we’d love to hear from you. Apply today.
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, Massachusetts, United States, AMER
From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.
About Us
Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data.
Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on.
Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions.
As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact.
During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.
About the Role
Fivetran + dbt is seeking a Manager of Security Operations, Incident Detection and Response, to bolster and improve the robustness and security of the platform and the entire enterprise. This people-leadership role is hands-on and execution-focused, requiring deep expertise in security operations, incident detection and response, AI-driven security automation, and securing multi-tenant SaaS environments. You'll build and lead a high-performing team, own the detection and response technology stack, and serve as the accountable leader for incident response across the entire enterprise, ensuring threats are detected, contained, and remediated quickly from endpoint to cloud to production.
This is a full-time remote position based out of the US (EST Time Zone).
Technologies You’ll Use
Crowdstrike, Okta, Google Workspace, Wiz, Open Source, Notion, JIRA
What You’ll Do
People Leadership:
Lead, mentor, and develop a team of detection and response engineers and analysts. Set clear goals, manage performance, and grow careers while fostering a culture of ownership, curiosity, and urgency.
Hiring & Team Building:
Recruit, hire, and onboard top security talent. Define team structure, roles, on-call rotations, and hiring plans to scale the team alongside the business.
Enterprise Incident Response:
Own the incident response program for the entire enterprise, including investigation, containment, eradication, remediation, and post-incident review. Act as incident commander for major security events and coordinate with Legal, Privacy, Communications, and executive leadership.
Detection Engineering:
Build, optimize, and maintain high-fidelity detections across cloud, identity, endpoint, SaaS, and application layers, using threat-informed approaches such as MITRE ATT&CK to close coverage gaps.
AI-Driven Security Operations:
Drive the adoption of AI, including LLMs and AI agents, to automate alert triage, enrichment, investigation, and response. Evaluate and integrate AI capabilities to scale the team, and build detections for threats targeting AI systems and AI usage across the enterprise.
Multi-Tenant SaaS Security:
Design detection and response strategies tailored to a multi-tenant SaaS platform, including tenant isolation, cross-tenant access, and customer data protection. Lead customer-impacting incident response and coordinate customer notifications.
Security Technology Ownership:
Own the security operations technology stack, including SIEM, SOAR, EDR/XDR, cloud detection and response, and log pipelines. Manage the roadmap, budget, and vendor relationships.
Threat Hunting & Intelligence:
Lead proactive threat hunting and integrate threat intelligence to identify and mitigate emerging threats before they impact the business.
Cross-Team Collaboration:
Work directly with engineering, infrastructure, IT, application security, and GRC teams to integrate detection and response practices and remediate risks across build pipelines, runtime environments, and corporate systems.
Tooling & Automation:
Develop and maintain automation, playbooks, and integrations to streamline security processes and provide self-service security tools to the organization.
Security Standards & Metrics:
Implement consistent security controls, monitoring, and alerting across the enterprise. Define and report on key metrics such as mean time to detect (MTTD) and mean time to respond (MTTR), and support compliance requirements.
Knowledge Sharing:
Stay up to date with trends in security operations, incident response, cloud security, and AI security, applying this knowledge to improve the Fivetran platform's security posture.
Skills We’re Looking For
Security Operations & Incident Response Expertise:
Deep expertise in detecting, investigating, and responding to security incidents, with a track record of leading enterprise-wide incident response.
People Management:
Proven experience managing, hiring, and developing security teams, including setting priorities, managing performance, and building a strong team culture.
Multi-Tenant SaaS Experience:
Extensive experience securing and responding to incidents in multi-tenant SaaS environments at scale, with a strong understanding of tenant isolation and customer data protection.
AI Proficiency:
Hands-on experience applying AI, including LLMs and AI agents, to security operations to automate detection, triage, and response, and a strong understanding of the risks AI introduces.
Cloud & Infrastructure Security:
Strong experience with cloud platforms (e.g., AWS, GCP, Azure), Kubernetes, and containerized environments, including cloud-native logging and detection.
Detection & Response Tooling:
Proficiency with SIEM, SOAR, EDR/XDR, identity, and cloud detection tools, and experience integrating them into automated workflows.
Technology Ownership:
Experience owning a security technology roadmap, including tool selection, budgets, and vendor management.
Problem-Solving:
Excellent problem-solving and troubleshooting skills, with the ability to make sound decisions under pressure in fast-paced environments.
Communication:
Strong communication skills with the ability to effectively collaborate with and educate engineering teams, and to communicate clearly with executives and customers during incidents.
Curiosity & Urgency:
Demonstrates strong curiosity, a sense of urgency, and a hands-on approach to diving deep into complex technical problems to drive timely and effective solutions.
#LI-Remote
#LI-AM1
The compensation range displayed on this job posting reflects the minimum and maximum target for new hire compensation for the target position and level, and may include sales incentives or target bonuses depending on the role. Our compensation ranges are determined by role, level, and location. Our job titles may span more than one career level. Within the range, individual compensation is determined by additional factors, including job-related skills, experience, relevant education or training, business need, market demands. The compensation range is subject to change and may be modified in the future. Your recruiter can share more about the specific compensation range for your location during the hiring process.
Pay Range
$186,701
—
$233,376 USD
Perks and Benefits
100% employer-paid medical insurance
*
Generous paid time away from work, including paid vacation or flexible/unlimited vacation depending on role and country, plus paid sick time, inclusive parental leave, paid holidays, including a year-end Global Week of Rest, and volunteer days off.
RSU stock grants*
Professional development and training opportunities
Company virtual happy hours, free food, and fun team-building activities
Monthly cell phone stipend
Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self-guided mindfulness exercises for all covered employees and their covered dependents.
*
May vary by country and worker type - please reach out to your recruiter for more information
Click
here
to learn more about Fivetran's Benefits by Region.
We’re honored to be
valued at over $5.6 billion
, but more importantly, we’re proud of our
core values of Get Stuck In, Do the Right Thing, and One Team, One Dream
. Read about us in
Forbes
.
Fivetran brings together high-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion & belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world-class company to better serve our customers, our people and our communities.
To learn more about Fivetran’s culture and what it’s like to be part of the team,
click here
and enjoy our video.
To learn more about our candidate privacy policy, you can
read our statement here
.
We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this
form
. We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.
Location: Remote, New York, United States, AMER
From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.
About Us
Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data.
Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on.
Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions.
As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact.
During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.
About the Role
Fivetran + dbt is seeking a Manager of Security Operations, Incident Detection and Response, to bolster and improve the robustness and security of the platform and the entire enterprise. This people-leadership role is hands-on and execution-focused, requiring deep expertise in security operations, incident detection and response, AI-driven security automation, and securing multi-tenant SaaS environments. You'll build and lead a high-performing team, own the detection and response technology stack, and serve as the accountable leader for incident response across the entire enterprise, ensuring threats are detected, contained, and remediated quickly from endpoint to cloud to production.
This is a full-time remote position based out of the US (EST Time Zone).
Technologies You’ll Use
Crowdstrike, Okta, Google Workspace, Wiz, Open Source, Notion, JIRA
What You’ll Do
People Leadership:
Lead, mentor, and develop a team of detection and response engineers and analysts. Set clear goals, manage performance, and grow careers while fostering a culture of ownership, curiosity, and urgency.
Hiring & Team Building:
Recruit, hire, and onboard top security talent. Define team structure, roles, on-call rotations, and hiring plans to scale the team alongside the business.
Enterprise Incident Response:
Own the incident response program for the entire enterprise, including investigation, containment, eradication, remediation, and post-incident review. Act as incident commander for major security events and coordinate with Legal, Privacy, Communications, and executive leadership.
Detection Engineering:
Build, optimize, and maintain high-fidelity detections across cloud, identity, endpoint, SaaS, and application layers, using threat-informed approaches such as MITRE ATT&CK to close coverage gaps.
AI-Driven Security Operations:
Drive the adoption of AI, including LLMs and AI agents, to automate alert triage, enrichment, investigation, and response. Evaluate and integrate AI capabilities to scale the team, and build detections for threats targeting AI systems and AI usage across the enterprise.
Multi-Tenant SaaS Security:
Design detection and response strategies tailored to a multi-tenant SaaS platform, including tenant isolation, cross-tenant access, and customer data protection. Lead customer-impacting incident response and coordinate customer notifications.
Security Technology Ownership:
Own the security operations technology stack, including SIEM, SOAR, EDR/XDR, cloud detection and response, and log pipelines. Manage the roadmap, budget, and vendor relationships.
Threat Hunting & Intelligence:
Lead proactive threat hunting and integrate threat intelligence to identify and mitigate emerging threats before they impact the business.
Cross-Team Collaboration:
Work directly with engineering, infrastructure, IT, application security, and GRC teams to integrate detection and response practices and remediate risks across build pipelines, runtime environments, and corporate systems.
Tooling & Automation:
Develop and maintain automation, playbooks, and integrations to streamline security processes and provide self-service security tools to the organization.
Security Standards & Metrics:
Implement consistent security controls, monitoring, and alerting across the enterprise. Define and report on key metrics such as mean time to detect (MTTD) and mean time to respond (MTTR), and support compliance requirements.
Knowledge Sharing:
Stay up to date with trends in security operations, incident response, cloud security, and AI security, applying this knowledge to improve the Fivetran platform's security posture.
Skills We’re Looking For
Security Operations & Incident Response Expertise:
Deep expertise in detecting, investigating, and responding to security incidents, with a track record of leading enterprise-wide incident response.
People Management:
Proven experience managing, hiring, and developing security teams, including setting priorities, managing performance, and building a strong team culture.
Multi-Tenant SaaS Experience:
Extensive experience securing and responding to incidents in multi-tenant SaaS environments at scale, with a strong understanding of tenant isolation and customer data protection.
AI Proficiency:
Hands-on experience applying AI, including LLMs and AI agents, to security operations to automate detection, triage, and response, and a strong understanding of the risks AI introduces.
Cloud & Infrastructure Security:
Strong experience with cloud platforms (e.g., AWS, GCP, Azure), Kubernetes, and containerized environments, including cloud-native logging and detection.
Detection & Response Tooling:
Proficiency with SIEM, SOAR, EDR/XDR, identity, and cloud detection tools, and experience integrating them into automated workflows.
Technology Ownership:
Experience owning a security technology roadmap, including tool selection, budgets, and vendor management.
Problem-Solving:
Excellent problem-solving and troubleshooting skills, with the ability to make sound decisions under pressure in fast-paced environments.
Communication:
Strong communication skills with the ability to effectively collaborate with and educate engineering teams, and to communicate clearly with executives and customers during incidents.
Curiosity & Urgency:
Demonstrates strong curiosity, a sense of urgency, and a hands-on approach to diving deep into complex technical problems to drive timely and effective solutions.
#LI-Remote
#LI-AM1
The compensation range displayed on this job posting reflects the minimum and maximum target for new hire compensation for the target position and level, and may include sales incentives or target bonuses depending on the role. Our compensation ranges are determined by role, level, and location. Our job titles may span more than one career level. Within the range, individual compensation is determined by additional factors, including job-related skills, experience, relevant education or training, business need, market demands. The compensation range is subject to change and may be modified in the future. Your recruiter can share more about the specific compensation range for your location during the hiring process.
Pay Range
$186,701
—
$233,376 USD
Perks and Benefits
100% employer-paid medical insurance
*
Generous paid time away from work, including paid vacation or flexible/unlimited vacation depending on role and country, plus paid sick time, inclusive parental leave, paid holidays, including a year-end Global Week of Rest, and volunteer days off.
RSU stock grants*
Professional development and training opportunities
Company virtual happy hours, free food, and fun team-building activities
Monthly cell phone stipend
Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self-guided mindfulness exercises for all covered employees and their covered dependents.
*
May vary by country and worker type - please reach out to your recruiter for more information
Click
here
to learn more about Fivetran's Benefits by Region.
We’re honored to be
valued at over $5.6 billion
, but more importantly, we’re proud of our
core values of Get Stuck In, Do the Right Thing, and One Team, One Dream
. Read about us in
Forbes
.
Fivetran brings together high-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion & belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world-class company to better serve our customers, our people and our communities.
To learn more about Fivetran’s culture and what it’s like to be part of the team,
click here
and enjoy our video.
To learn more about our candidate privacy policy, you can
read our statement here
.
We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this
form
. We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.
Location: (Multiple States), United Kingdom
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), Germany
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), France
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), Ireland
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), Spain
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), Portugal
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), Netherlands
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
San Francisco; New York City
Canada
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Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
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