What You'll Do:

- Own end-to-end analysis for product areas such as host onboarding, supply growth, booking funnel conversion, fulfilment rate, and pricing - from problem framing to insight to recommendation.

- Design, execute, and interpret A/B experiments (sample sizing, metric selection, guardrails, statistical significance) to validate product and policy changes.

- Build and maintain dashboards and self-serve reporting (Tableau/Looker) that give PMs and business stakeholders real-time visibility into key metrics.

- Write and optimize complex SQL (BigQuery/PostgreSQL) against large, high-cardinality datasets - bookings, fleet telemetry, support tickets, funnel events.

- Partner with PMs to define success metrics and North Star/HEART-style frameworks for new features before they ship.

- Conduct root-cause and cohort/retention analysis to explain shifts in cancellation rate, fulfilment rate, host retention, or conversion.

- Translate ambiguous business questions into structured analyses and clear, actionable narratives for leadership.

- Collaborate with Engineering and Data Eng on instrumentation, data quality, and pipeline issues that affect trustworthy reporting.

What We're Looking For:

- 5+ years of experience in product analytics, business analytics, or a similar data-facing role, ideally at a marketplace, on-demand, or consumer tech company.

- Strong SQL skills - comfortable with window functions, UNNEST/ARRAY handling, joins across large tables, and query optimization.

- Working proficiency in Python (pandas) for data manipulation and ad hoc analysis.

- Solid grounding in experimentation methodology - sample size calculation, statistical significance, SUTVA/interference considerations, stratified randomization.

- Familiarity with product analytics frameworks (AARRR, funnel/cohort/retention analysis, NSM/HEART) and the judgment to know which one fits which problem.

- Experience with BI tools (Tableau, Looker, or similar) for building dashboards stakeholders actually use.

- Ability to communicate analysis crisply to both technical and non-technical audiences - a good analyst here is also a good storyteller.

- Bias toward ownership: comfortable working with ambiguity, prioritizing across multiple concurrent workstreams, and driving analyses to a decision, not just a chart.

Good to Have:

- Exposure to marketplace/two-sided platform dynamics (supply-demand balancing, pricing elasticity, host/guest economics).

- Experience with BigQuery specifically, or other cloud data warehouses.

- Prior work on support/ops analytics (ticket taxonomy, contact rate reduction) or fleet/logistics data.

- Familiarity with LLM-assisted analytics tooling (e.g., natural-language-to-SQL interfaces) as a productivity multiplier.