Senior Data Analyst - Marketing

From the listing

About the role

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: 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.
• 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 an experimentation roadmap, embedding it 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, 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.
• 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.

What they're looking for

• 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 rather than compete.
• 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: 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.
• Strong business acumen and fluency in growth metrics: CAC, LTV, payback period, conversion rates, funnel performance.
• Think in terms of self-service and scale: build tools and frameworks that make marketing teams independently capable, not become the bottleneck for every "did this work" question.
• Can hold technical and strategic context at once: as comfortable in a model's residuals as in a conversation about budget tradeoffs.
• AI-forward in how you work: 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 have

• 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).

Benefits, as listed

1 of these also appear in the perks Jeff verified on the company page.