Data Engineering Manager, Data & ML Platform

About the role

Hinge Health is building the data and ML backbone that powers personalized MSK care for millions of members. As a Data Engineering Manager, you will lead the Data & ML Platform team, shaping how data flows consistently across systems and how product and Data Science teams build on top of it.

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.

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field.
• A minimum of 2 years of hands-on data engineering experience, building and operating production data pipelines, data platforms, and data infrastructure at scale.
• A minimum of 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).

Qualifications

• 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.

Skills

• Strong data modeling instincts.
• Ability to bridge both worlds between data engineering and ML.
• Experience with streaming systems like Kafka, Flink, Spark.
• Proficiency with Python, SQL, Spark, dbt, Databricks, and AWS.
• Experience with ML platforms like feature pipelines, feature stores, model serving, and ML workflow infrastructure.
• Knowledge of data contracts, orchestration, and data quality.
• Experience with regulated environments (e.g., HIPAA, SOC 2).
• Experience with Databricks ecosystem (Delta Lake, MLflow, Unity Catalog).
• Experience with AI tools and AI-native practices.

Benefits

Inclusive healthcare and benefits: comprehensive medical, dental, and vision coverage, gender-affirming care, tools for family and fertility planning, travel reimbursements.

Planning for the future: traditional or Roth 401(k) retirement plan options with a 2% company match.

Modern life stipends: support for learning and development.

Culture & Equal Opportunity: Hinge Health is an equal opportunity employer and prohibits discrimination and harassment of any kind. We make employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability status, pregnancy, or any other basis protected by federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We provide reasonable accommodations for candidates with disabilities. If you feel you need assistance or an accommodation due to a disability, please let us know by reaching out to your recruiter.