About the role We're looking for a Full-Cycle Data Engineer to join our Data & AI team and own the flow from product data sources → modeling → dashboards → insights. You'll partner with product managers, engineers, and AI teams to turn raw product data into reliable analytics infrastructure that drives decisions across the company — from individual feature bets to CEO- and CFO-level questions. This is an end-to-end role: you'll take data products from ideation through engineering, analytics, and production deployment. Key responsibilities Pipelines & infrastructure Design, build, and deploy scalable data pipelines from product and system sources in production, using Python and orchestrators like Airflow. Work with distributed query engines such as BigQuery or Athena, with strong SQL throughout. Build and maintain semantic data models for large-scale operational systems and data lakes, manually or with tooling like dbt. Improve the end-to-end analytics stack, from ingestion to visualization, and collaborate with engineering on event tracking and instrumentation. Ensure data quality, consistency, and reliability across the stack. Analytics & reporting Build and maintain dashboards and reporting layers in tools like Looker or Metabase, optimized for performance, usability, and clarity Create self-serve analytics so product and business stakeholders can answer their own questions Support product experimentation: A/B testing, funnel analysis, feature adoption Partnership & insight Translate ambiguous questions from product leads, the CEO, the CFO, and others into clear metrics, KPIs, and analytical models Surface trends in usage and user behavior that influence the product roadmap and feature prioritization Provide ad-hoc analysis and strategic reporting for leadership Requirements 5+ years in data engineering, data analytics, or product analytics Strong SQL and hands-on experience with large-scale datasets in cloud data warehouses (BigQuery or similar) Pro
