Senior Data Analyst

Senior Data Analyst About Haus Haus is the incrementality platform leading brands trust to optimize billions in ad spend worldwide. Using frontier causal inference-based econometric models to run experiments, we help brands measure the business impact of marketing, pricing, and promotions with scientific precision. Over $360B is spent annually on paid advertising in the US alone, and the famous quote “half the money I spend on advertising is wasted; the trouble is I don't know which half” still rings true. Haus helps marketers identify which half, and reallocate it to maximize growth. With a founding team of former product managers, economists, and engineers from Google, Netflix, Meta, and Amazon, we make high-quality decision science, incrementality testing, and causal marketing mix modeling accessible to businesses of all sizes—automating the heavy lifting of experiment design, data processing, and insights generation. Haus works with leading brands like FanDuel, Sonos, and Dr. Squatch, delivering ROI gains as high as 30x. Haus is well-capitalized and backed by top-tier VCs, including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized by LinkedIn as a 2025 Top Startup https://www.linkedin.com/pulse/linkedin-top-startups-2025-50-us-companies-rise-linkedin-news-hox6f/! WHAT YOU'LL DO You’ll be the first dedicated analytics hire at Haus, working directly with the Director of Data & Analytics to build the analytics function. This is a high-leverage role: you’ll touch everything from GTM pipeline reporting to product engagement analysis to the dbt models and warehouse layer underneath. We’re looking for a true generalist who can context-switch fluidly, move fast with modern AI-assisted tooling, and translate data into decisions that executives and cross-functional partners actually act on. RESPONSIBILITIES GTM Analytics - Build and own pipeline, conversion, and ROI analytics in partnership with Revenue Operations, Marketing, Sales, and Customer Success. - Develop forecasting models for new business acquisition and retention cohorts. - Stand up and own the data warehouse and metric layer (Fivetran for pipelines, dbt for transformations) with GTM as the first domain. - Build scalable, well-tested dbt models that serve as the single source of truth for business metrics. Product Analytics - Analyze feature adoption, engagement patterns, and retention indicators across the product surface. - Build customer health views—both at a portfolio level and drilled into specific product areas. - Partner with Product and Engineering to instrument events and ensure data quality at the source. - Extend the dbt layer and BI tooling (Looker, Mode, Hex, or Tableau) to serve self-serve product analytics across the org. Stakeholder Partnership - Partner with cross-functional leaders (GTM, Product, Finance, Exec team) to embed data into daily decisions. - Translate complex analysis into clear, exec-ready narratives—not just dashboards. - Champion data literacy and build a self-serve analytics culture across the org. QUALIFICATIONS - 4–7 years in analytics, data, or analytics engineering roles (B2B SaaS strongly preferred). - Hands-on fluency in SQL and dbt; comfort with data modeling and warehouse architecture. - Experience with modern ELT tools (Fivetran or equivalent) and at least one BI platform (Looker, Mode, Hex, Tableau). - Already using AI-assisted coding tools (Claude Code, Codex, etc.) in your day-to-day work. Not just experimenting, but relying on them to ship faster. - Strong stakeholder communication: you can present to a CRO on Monday and pair with an engineer on Tuesday. - Comfort operating as the first analytics hire—you thrive with ambiguity, ownership, and building from zero. BONUS POINTS - Python proficiency for analysis, scripting, or lightweight ML/statistical modeling. - Experience standing up or significantly evolving a data warehouse (no ... (truncated, view full listing at source)