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 ’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 both worlds. Employees in hybrid roles are required to be in the office 3 days per week, for the full 8 hours of a typical business day. The San Francisco office has a dog-friendly workplace program.

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 team.

In your first 6 months, you will:

• Lead the evolution of our data platform toward a streaming-first, ML-ready architecture, improving data freshness, consistency, and discoverability across domains.

• Design and deliver the first iteration of our ML platform layer — feature pipelines, feature store, and model serving patterns — enabling Data Science teams to self-serve within shared governance and operational standards.

• Drive schema governance and data contracts with upstream service teams to reduce fragmentation, standardize core data models, and improve reliability for downstream analytics and ML consumers.

• Invest in developer productivity: 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 raises the bar on reliability, scalability, and developer experience.

• Institutionalize operational rigor (SLOs, incident management, observability, change management) appropriate for a HIPAA/SOC 2–oriented environment, in close partnership with Security and Compliance.

• Data platform-first, ML-fluent: Your roots are in data engineering and data platforms, and you’re equally comfortable thinking about data modeling, schema evolution, data contracts, orchestration, and data quality as you are about feature stores, model serving, and ML workflows.

• Product-minded systems thinker: You don’t build infrastructure in a vacuum; you seek to understand the analytics, product, and ML use cases you’re enabling and…