Senior Data Analyst - Marketing

The role

This role sits within the Data Intelligence team at Supabase and partners with Marketing, Growth and channel owners. The analyst builds a trustworthy, causal view of what spend produces pipeline and revenue, replacing reliance on ad-platform numbers. It is a ground-floor opportunity suited to autonomous, async, AI-oriented workers.

What you would do

• Lead the overall measurement approach for paid and product-led marketing, spanning testing, MMM and attribution
• Define how ad-platform figures, multi-touch models, MMM and experiments combine to guide spending choices
• Convert model findings into advice on where to increase or reduce spend against CAC, payback and LTV:CAC goals
• Run continuous user-level and geo-level incrementality tests and compute lift, incrementality share and incremental ROAS/CPA
• Create reusable templates and playbooks so experiment design and readouts stay consistent across teams
• Work with owners of search, social, lifecycle and SEO to set priorities for a testing roadmap
• Develop and maintain media mix models that account for seasonality, adstock and saturation, validated by backtests and experiment results
• Produce budget scenarios and forecasts by channel and region that non-technical partners can use
• Document metric definitions and analysis patterns so marketers can self-serve, and use AI tools daily
• Detect and fix root causes of tracking, spend and export data problems with other teams

What they asked for

• Around six or more years working in marketing analytics, data science or similar, centered on performance marketing or PLG growth
• Solid grasp of attribution, incrementality testing and MMM and how they reinforce one another
• Advanced SQL plus proficiency in Python or R for modeling and experiment evaluation
• Practical experience designing and interpreting experiments over search, social, display, email and in-product channels
• Background building or closely supporting MMM or sophisticated attribution models, ideally in consumption-based or BaaS businesses
• Strong command of growth metrics such as CAC, LTV, payback, conversion and funnel results
• Habit of building self-serve tools and frameworks instead of acting as a bottleneck
• Regular use of LLMs and AI tooling with a considered view of their impact on analysis
• Ability to explain causal subtleties clearly to non-technical audiences
• Comfort working autonomously and asynchronously while building a function from zero

Nice to have

• Prior work at BaaS, DevRel or open-source developer tool companies linking marketing spend to pipeline and revenue
• Knowledge of econometrics, time-series methods or Bayesian approaches for MMM and testing
• Familiarity with Google Ads, Meta, LinkedIn, web analytics, CDPs and BI tools