Data Engineering Manager, Data & ML Platform

Hinge Health•San Francisco, CA

1d•$220,000 - $330,000•Hybrid

About The Position

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.

Requirements

5+ years of hands-on data engineering experience, building and operating production data pipelines, data platforms, and data infrastructure at scale.
2+ years of experience managing engineering teams, with a track record of hiring, developing, and retaining technical talent.
2+ years of experience building ML platform capabilities (e.g., feature pipelines, feature stores, model serving, or ML workflow infrastructure) in a production environment.
Experience building data platforms across batch and streaming systems, including technologies such as Kafka, Flink, Spark, or equivalent.
Proficiency with a modern data stack such as Python, SQL, Spark, dbt, Databricks, and AWS (or comparable tools), and comfort evaluating new technologies in this space.
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 design platforms that are intuitive, safe, and flexible for your customers.
0→1 / 1→10 builder: You’ve stood up ML platform capabilities in a growth-stage or scaling company where systems were evolving and not fully mature — building patterns, not just operating pre-built infrastructure.
Operationally rigorous: You treat reliability, observability, incident response, and guardrails in regulated environments as first-class product features of the platform.
AI-forward engineering leader: You’re excited about AI-assisted development workflows and can coach your team on using AI tools to move faster while maintaining high engineering standards.
People-first manager: You hire and develop strong technical talent, give clear direction, and create an environment where engineers can do the best work of their careers.

Nice To Haves

Experience standing up ML platform capabilities in a growth-stage or scaling environment, taking systems from 0→1 or 1→10, rather than only operating fully mature platforms at very large companies.
Demonstrated deep data platform fluency across data modeling, schema evolution, data contracts, pipeline orchestration, and data quality — with ML platform work as a natural extension of that foundation.
Strong product and business curiosity: you quickly learn the domain, understand how analytics and ML drive outcomes, and translate Data Science needs into clear engineering execution.
Background in regulated environments (e.g., HIPAA, SOC 2 or similar), with a strong orientation toward SLOs, observability, and incident management.
Experience with the Databricks ecosystem (Delta Lake, MLflow, Unity Catalog) or similar technologies.
Demonstrated AI-forward mindset, including experience incorporating AI tools into engineering workflows and mentoring teams on effective, safe AI-native practices.

Responsibilities

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.
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.
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.