Hinge HealthSan Francisco, CAStaff Data Engineer leading architecture and reliability for a unified data platform that powers real-time experiences, analytics, and ML systems. Owns data contracts, schema evolution, observability, and standards across Kafka, Flink, PySpark, Delta Lake, and Databricks in a HIPAA-compliant environment.
Own data platform architecture and technical direction: Lead the design of systems that span the full pipeline stack — raw ingestion, streaming and batch transformations, analytical models, and the serving layer that downstream consumers depend on. Make architectural decisions that balance reliability, performance, cost, and long-term maintainability. Set the patterns and standards that other engineers follow.
Lead the hardest cross-functional technical problems: Drive complex initiatives that span multiple teams and services. Define data contracts with upstream producers, lead schema evolution strategies, and resolve systemic friction between data producers and consumers. Be the person who steps in when a problem is too ambiguous or cross-cutting for a single team to solve.
Raise the engineering bar across the team: Set and enforce standards for data modeling, pipeline reliability, testing practices, code quality, and operational excellence. Mentor senior engineers through design reviews, pairing, and technical coaching. Influence how the team thinks about building systems, not just what they build.
Keep the platform reliable and the data trustworthy: Own the reliability posture of the most critical data systems. Define and drive SLAs and SLOs for key pipelines, lead incident response for complex data failures, and drive the systemic fixes — not just the immediate patches. Champion observability, data quality, and operational rigor as first-class concerns.
Make it easy for teams to own their data: Build tooling and establish practices that enable service and application teams to effectively manage their data. Coach teams on compliance strategies, performance tuning, event-driven design, and schema evolution so they can take ownership without creating bottlenecks on the data team.
Deliver with ownership and grit: Take the most ambiguous, highest-stakes projects from problem definition to production. Work through technical blockers, cross-functional dependencies, and competing priorities. Keep stakeholders informed and build a track record of delivering high-quality data assets that teams trust and depend on.
Python, SQL, Flink, PySpark, Kafka, Delta Lake, dbt, Airflow, Aurora PostgreSQL, AWS, Databricks
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