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ABOUT THE ROLE As a Staff Data Engineer, you will be a technical leader on the Data & ML Platform team, owning the architecture and reliability of the data infrastructure that powers real-time member experiences, analytics, and AI across Hinge Health. Where a Senior Data Engineer owns individual pipelines and systems, you own the patterns, standards, and architectural decisions that shape how the entire platform works. You will design systems that move data from dozens of upstream services — across Kafka event streams and transactional databases — into a unified data platform that serves real-time APIs, analytical workloads, and ML systems on Databricks. You will lead the most complex technical initiatives end-to-end, drive alignment across teams, and make the architectural calls that other engineers build on. You will define data contracts, own schema evolution strategy, and set the standards for how data is modeled, tested, and governed across the platform. You'll work in a modern stack: Python, SQL, Flink, PySpark, Kafka, Delta Lake, dbt, Airflow, Aurora PostgreSQL, and AWS. This is a high-ownership role where you operate with significant autonomy. You'll work closely with application engineers, data scientists, product teams, and SRE — not just building systems, but shaping how other teams produce and consume data reliably in a HIPAA-compliant environment. WHAT YOU'LL ACCOMPLISH - 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. BASIC QUALIFICATIONS - Bachelor’s Degree (or equivalent) in Computer Science, Engineering, or a related technical field. - A minimum of 4 + years of data engineering experience with a proven track record of building and operating reliable production data platforms at scale. - 4+ years of strong proficiency in Python and SQL. - 4+ years of experience with distributed data processing frameworks