Data Engineering Manager, Data & ML Platform

About the role

You own the data and ML platform strategy and execution that powers Hinge Health's personalized musculoskeletal care at scale. You will define and lead the evolution of a streaming-first, ML-ready architecture that ensures data flows reliably and consistently across all systems. You will partner deeply with Data Science and Product teams to understand critical use cases and translate them into robust platform capabilities. You will stabilize core pipelines and on-call practices while establishing clear standards for reliability, observability, and data quality. You will design and deliver the foundational ML platform layer, including feature pipelines, feature store, and model serving patterns. You will embed operational rigor and governance into the platform to support HIPAA and SOC 2 requirements. You will build, mentor, and retain a high-performing data engineering team that raises the bar for execution and developer experience.

Key facts

What you'll do

• Map and modernize our current data and ML platform landscape, including batch and streaming pipelines, data models, orchestration, and data quality posture for analytics and production systems.
• Establish clear ownership of core pipelines and services, driving reliability improvements, on-call practices, SLO definition, and observability baselines for the data platform team.
• Lead the design and delivery of a streaming-first, ML-ready architecture that improves data freshness, consistency, and discoverability across domains.
• Build the first iteration of the ML platform layer, including feature pipelines, feature store abstractions, and model serving patterns that enable self-serve ML with shared governance.
• Define and implement schema governance and data contracts with upstream service teams to reduce fragmentation and standardize core data models.
• Partner with Data Science to operationalize machine learning models end to end, covering feature pipelines, serving, monitoring, and retraining workflows.
• Invest heavily in developer productivity by introducing tooling, templates, CI/CD pipelines, and testing practices that make it easy for product and ML teams to build on the platform.
• Act as a technical and people leader, setting direction, prioritizing trade-offs, and communicating platform decisions to both technical and business stakeholders.
• Embed operational rigor as a first-class platform feature, including incident management, observability, change management, and compliance practices for a HIPAA/SOC 2 environment.
• Mentor and grow a high-performing data engineering team, fostering clarity of ownership, strong execution habits, and a culture of reliability, scalability, and developer experience.

Requirements

• Data platform-first mindset with strong ML fluency, comfortable navigating data modeling, schema evolution, data contracts, orchestration, and data quality alongside feature stores, model serving, and ML workflows.
• Product-minded systems thinker who understands analytics, product, and ML use cases and designs platforms that are intuitive, safe, and flexible for internal customers.
• Experience building ML platform capabilities in a growth-stage or scaling company where systems were immature and you drove 0→1 and 1→10 platform patterns.
• Operationally rigorous approach to reliability, observability, incident response, and guardrails, especially in regulated environments.
• AI-forward engineering leader who is excited about AI-assisted development workflows and can coach a team on using these tools effectively.
• People-first manager who invests in hiring, mentoring, and developing strong technical talent and sets a high bar for execution and ownership.
• Comfortable making foundational architecture decisions and partnering with Data Science to operationalize models in production.
• Deep engagement with both technical and non-technical stakeholders to align on priorities and communicate trade-offs clearly.
• Proven ability to own end-to-end data and ML platform strategy, including roadmap planning, architecture, and operational excellence for streaming, batch, and ML workloads.

Practical notes

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.