Data Engineering Manager, Data & ML Platform

The Opportunity Hinge Health is building the data and ML backbone that powers personalized MSK care for millions of members — from real-time product experiences to clinical insights and cost savings for our customers. As a Data Engineering Manager leading our Data & ML Platform team, you’ll sit at the intersection of data engineering, real-time systems, and ML enablement, owning the platforms that make analytics, experimentation, and machine learning reliable at scale. You’ll guide our evolution toward a streaming-first, ML-ready architecture, shaping how data flows consistently across systems and how product and Data Science teams build on top of it — all in service of reducing pain and improving movement for people around the world. This is not a pure infrastructure or ML engineering role. We’re looking for a data platform leader with strong data modeling instincts, product awareness, and enough ML platform experience to bridge both worlds. Our data platform is maturing and our ML platform capabilities are still early — you’ll make foundational architecture decisions, partner with Data Science to operationalize models, and lead both the team and the technical direction as a tech lead manager. Hinge Health operates a hybrid model in San Francisco. We believe that remote work and in-person work have their own advantages and disadvantages, and we want to leverage the best of [... source excerpt omitted ...] ce 3 days per week, for the full 8 hours of a typical business day. The San Francisco office has a dog-friendly workplace program. What You’ll Accomplish In your first 3 months, you will: Deeply understand our current data and ML platform: batch and streaming pipelines, data models, orchestration, and data quality posture across analytics and production systems. Build strong partnerships with Data Science, Product, and other engineering teams; align on top ML and product use cases the platform must unlock. Take ownership of a subset of core pipelines and services, stabilizing reliability and on-call practices while establishing clear SLOs and observability baselines for the [... source excerpt omitted ...] uctivity: introduce tooling, templates, CI/CD, and testing practices that make it significantly easier for product and ML teams to build on the platform. In your first 12 months, you will: Own and evolve the end-to-end data & ML platform strategy, including roadmap, architecture, and operational excellence across streaming, batch, and ML workloads. Partner with Data Science to operationalize models in production — from feature pipelines to serving, monitoring, and retraining — and embed these workflows into our broader data ecosystem. Build, mentor, and retain a high-performing data engineering team, creating clarity of ownership, strong execution habits, and a culture that r