Senior Data Analyst - Marketing

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-s