Senior Data Analyst - Marketing
- Role
- Data Science
- Experience
- Senior
- Employment
- Full-time
- Company size
- Mid-size
Open to Worldwide. Set where you work from to check your eligibility.
No BS summary
Senior marketing data analyst with 6+ years in marketing analytics, data science, or related work. Must be advanced in SQL plus Python or R, with hands-on attribution, incrementality testing, media mix modeling, and performance marketing/PLG experience. Fully remote, global role.
Core skills
Required skills
Optional skills
About Supabase
Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth.
About the Role
We're looking for a Senior Data Analyst, Marketing to join our Data Intelligence team and build the measurement foundation that tells us what's actually working across paid and PLG channels. You'll work closely with Marketing, Growth, and channel owners, helping us move past platform-reported metrics and vanity numbers into causal, trusted answers about what drives pipeline and revenue.
This role is ideal for someone who thrives in async, fast-paced environments, is AI-forward in how they work, and is excited about building a measurement function from the ground up.
What You'll Be Responsible for
Marketing Measurement Strategy
- Own the end-to-end marketing measurement strategy across experimentation, media mix modeling, and attribution for paid and PLG channels
- Establish and evolve the attribution framework: how platform data, multi-touch attribution, MMM, and experiments work together to inform decisions
- Translate complex measurement outputs into clear recommendations on where to invest, what to cut, and how to hit pipeline, revenue, and efficiency targets (CAC, payback, LTV to CAC)
- Serve as the subject matter expert for marketing measurement, educating stakeholders on causality, model uncertainty, and the limitations of platform-reported metrics
Incrementality and Experimentation
- Design and run always-on incrementality tests, user-level and geo-level, to quantify the causal impact of key channels, campaigns, and tactics
- Calculate incremental lift, incrementality percent, and incremental ROAS/CPA, and use these to guide budget reallocation
- Build repeatable analysis templates and playbooks for experiment design, analysis, and readouts so results are consistent across teams
- Partner with channel owners (paid search, paid social, lifecycle, website/SEO) to build and prioritize a experimentation roadmap, embedding it into campaign planning, creative testing, and audience strategy
Media Mix Modeling and Forecasting
- Build and maintain media mix models using historical data to estimate channel contribution, marginal returns, and optimal budget allocation
- Incorporate seasonality, adstock, and saturation effects, and continuously validate model performance through backtesting and reconciliation with experiment results
- Turn MMM insights into budget scenarios and forecasts across channels and regions, communicated in a way non-technical stakeholders can act on
Context, Tooling, and Data Integrity
- Own and build the context and skills that let marketing teams run accurate self-serve analytics: metric definitions, model documentation, and reusable analysis patterns, not just dashboards
- Use AI tools as a core part of daily work to accelerate analysis and go deeper than a traditional analyst workflow allows, and help establish AI-forward practices across the marketing org
- Identify gaps and inconsistencies in marketing data (tracking, spend, platform exports) and work cross-functionally to fix them at the root rather than patching around them downstream
You Might Be a Good Fit If You
- Have 6+ years in marketing analytics, data science, or a related role, with a focus on performance marketing and/or PLG growth
- Deeply understand marketing attribution, incrementality testing, and media mix modeling, and how they complement each other rather than compete
- Are advanced in SQL and at least one statistical programming language (Python or R) for experiment analysis and modeling
- Have hands-on experience designing, running, and interpreting experiments across digital marketing channels: search, social, display, email, in-product
- Have built or worked closely with MMM and/or advanced attribution models, ideally in a consumption or BaaS environment
- Have strong business acumen and fluency in growth metrics: CAC, LTV, payback period, conversion rates, funnel performance
- Think in terms of self-service and scale: your instinct is to build tools and frameworks that make marketing teams independently capable, not to become the bottleneck for every "did this work" question
- Can hold technical and strategic context at once: you're as comfortable in a model's residuals as you are in a conversation about budget tradeoffs
- Are AI-forward in how you work: you use LLMs and AI tooling habitually and have a clear point of view on how it changes what an analyst can do
- Communicate clearly to non-technical stakeholders and know how to make causal nuance land in a business conversation
- Thrive in async, autonomous environments and are energized by building a measurement function from the ground up
Nice to Haves
- Experience in BaaS, DevRel or open source dev tool companies, connecting marketing spend to pipeline and revenue outcomes
- Experience with applied econometrics, time-series modeling, or Bayesian methods for MMM and experimentation
- Familiarity with common marketing and analytics tools (Google Ads, Meta, LinkedIn, web analytics, CDPs, BI/visualization tools)
What We Offer
- Fully Remote We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world.
- ESOP Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together.
- Tech Allowance Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work.
- Health Benefits Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us.
- Annual Off-Sites Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year.
- Flexible Work We operate asynchronously and trust you to manage your own time. You know what needs to be done and when.
- Professional Development Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth.
About the Team
Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.
- ~400 team members
- 60+ countries
- 20+ languages spoken
- Over $1B raised (including our $500M Series F)
- 540,000+ community members
We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.
What you'll do
- Own the end-to-end marketing measurement strategy across experimentation, media mix modeling, and attribution for paid and PLG channels
- Establish and evolve the attribution framework across platform data, multi-touch attribution, MMM, and experiments
- Translate complex measurement outputs into clear recommendations on investment, cuts, pipeline, revenue, and efficiency targets including CAC, payback, and LTV to CAC
- Serve as the subject matter expert for marketing measurement and educate stakeholders on causality, model uncertainty, and limitations of platform-reported metrics
- Design and run always-on user-level and geo-level incrementality tests to quantify causal impact of key channels, campaigns, and tactics
- Calculate incremental lift, incrementality percent, and incremental ROAS/CPA to guide budget reallocation
- Build repeatable analysis templates and playbooks for experiment design, analysis, and readouts
- Partner with channel owners across paid search, paid social, lifecycle, and website/SEO to build and prioritize an experimentation roadmap
- Embed experimentation into campaign planning, creative testing, and audience strategy
- Build and maintain media mix models using historical data to estimate channel contribution, marginal returns, and optimal budget allocation
- Incorporate seasonality, adstock, and saturation effects into models
- Continuously validate model performance through backtesting and reconciliation with experiment results
- Turn MMM insights into budget scenarios and forecasts across channels and regions for non-technical stakeholders
- Own and build context and skills for accurate marketing self-serve analytics, including metric definitions, model documentation, and reusable analysis patterns
- Use AI tools as a core part of daily work to accelerate analysis and deepen analyst workflow
- Help establish AI-forward practices across the marketing organization
- Identify gaps and inconsistencies in marketing data including tracking, spend, and platform exports
- Work cross-functionally to fix marketing data issues at the root rather than patching downstream
What they require
- 6+ years in marketing analytics, data science, or a related role, with a focus on performance marketing and/or PLG growth
- Deep understanding of marketing attribution, incrementality testing, and media mix modeling, and how they complement each other
- Advanced SQL and at least one statistical programming language, Python or R, for experiment analysis and modeling
- Hands-on experience designing, running, and interpreting experiments across digital marketing channels including search, social, display, email, and in-product
- Experience building or working closely with MMM and/or advanced attribution models, ideally in a consumption or BaaS environment
- Strong business acumen and fluency in growth metrics including CAC, LTV, payback period, conversion rates, and funnel performance
- Ability to build tools and frameworks that make marketing teams independently capable rather than becoming the bottleneck
- Ability to hold technical and strategic context at once, from model residuals to budget tradeoffs
- AI-forward working style with habitual use of LLMs and AI tooling and a clear view on how it changes analyst work
- Clear communication to non-technical stakeholders and ability to make causal nuance land in business conversations
- Thrives in async, autonomous environments and is energized by building a measurement function from the ground up
- Preferred: Experience in BaaS, DevRel, or open source dev tool companies, connecting marketing spend to pipeline and revenue outcomes
- Preferred: Experience with applied econometrics, time-series modeling, or Bayesian methods for MMM and experimentation
Benefits
- Fully remote work
- Global hiring from anywhere
- WeWork membership or co-working allowance usable anywhere in the world
- ESOP equity ownership for every team member
- Tech allowance for laptop, monitor, headphones, or other work environment setup
- 100% health insurance coverage for employees
- 80% health insurance coverage for dependents
- Annual company off-sites in a new city
- Flexible asynchronous work with trust to manage your own time
- Annual education allowance for courses, books, conferences, or other professional development
open source backend platform for app development