Full-Cycle Data Engineer

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 ...