Senior Product Analyst - eCommerce

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.