Product Data Engineer, Docplanner

• 2+ years of experience in Data Engineering or a similar role
• Hands-on experience designing, scheduling, and maintaining ETL pipelines using Apache Airflow
• Strong SQL skills and solid understanding of data warehousing concepts (preferably Google BigQuery)
• Proficiency in Python for ETL development and automation
• Experience working in AI product environments, supporting data needs for AI features such as experimentation, monitoring, and analytics
• Experience integrating data from multiple sources (APIs, databases, flat files, external platforms)
• Experience building dashboards or analytical views using BI tools (preferably Looker)
• Familiarity with Google Cloud Platform (GCP) services
• Strong analytical and problem-solving skills
• Comfortable working in a cross-functional, ambiguous environment
• Strong communication skills and ability to collaborate with both technical and non-technical stakeholders
• Strong interest in product data and how data drives product decisions

Desirable

• We are looking for a Product Data Engineer to own and evolve our data platform at the intersection of Backend, BI, and Product
• This role is not purely analyst nor purely backend: it is a cross-functional ownership role responsible for ensuring that product data is reliable, well-modeled, and effectively consumed by BI and Product teams
• You will design and maintain ETL pipelines, collaborate closely with Backend and BI teams, and act as a bridge between data producers and data consumers, enabling high-quality analytics and product decision-making
• Data Platform & Pipelines:
• Design, build, and maintain reliable end-to-end ETL pipelines orchestrated with Apache Airflow
• Integrate data from multiple sources (internal operational databases, third-party APIs, SaaS tools) into the Google Cloud Data Warehouse (BigQuery)
• Design and evolve data models, warehouse schemas, and transformations to support scalable analytics and KPIs
• Ensure data quality, reliability, and observability through monitoring, validation, and alerting
• Product & Analytics Enablement:
• Own the product data structure, mapping product features and behaviors to analytics-ready data models
• Define and maintain meaningful KPIs in collaboration with Product and BI
• Enable analytics for AI-powered product features, ensuring visibility on usage, performance, quality, and business impact
• Partner with Product, BI, and other stakeholders to gather requirements and deliver dashboards and reports
• Maintain clear and up-to-date documentation for data models, pipelines, and metrics
• Backend ↔ BI Bridge & Ownership:
• Act as the primary bridge between Backend Engineering and BI, owning the flow from data production to analytics consumption
• Triage, analyze, and address BI requests related to data availability, correctness, performance, and modeling
• Collaborate with Backend Engineers on data contracts, schema evolution, and performance optimization, without owning core backend services
• Proactively identify and resolve data-related issues impacting BI and Product teams
• Platform Health & Support:
• Own first-level monitoring and support for data pipelines and Airflow DAGs, ensuring timely resolution of failures
• Collaborate with BI and Backend teams to troubleshoot and resolve complex issues
• Continuously improve the stability, performance, and maintainability of the data platform
• What This Role Is (and Is Not):
• Owns data pipelines, models, and analytics enablement
• Acts as a bridge between Backend, BI, and Product
• Does not own core backend services
• Is not a pure BI or reporting-only role