Senior Data Analyst - Marketing

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.

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