Senior Staff Data Engineer - Data & ML Platform

About the Role

We're looking for a Senior Staff Data Engineer to be the technical backbone of our Data & ML Platform team — the foundation powering analytics, product experiences, and machine learning across Hinge Health. This is a high-ownership IC role for someone who wants to set the technical vision for the platform, drive architecture across organizational boundaries, and shape how Hinge Health builds on data and ML for years to come.

Your scope extends beyond any single team or system. You'll own the most consequential architectural decisions across the data platform — how streaming and batch systems converge, how data models serve both analytical and ML workloads, and how the platform evolves as the company's AI ambitions scale. You'll work in a modern stack including Python, SQL, Spark, dbt, Kafka, Flink, Databricks, and AWS, and increasingly at the boundary where data platform meets ML platform — feature pipelines, serving layers, and the infrastructure that makes ML models production-ready.

This is not a role where you go deep on a single system. You'll operate across the full platform surface — identifying the highest-leverage technical problems the organization faces, driving alignment across engineering, Data Science, and product teams, and making architectural decisions that others build on. You should be equally comfortable authoring a platform-wide technical strategy, debugging a production incident, mentoring senior engineers, and explaining tradeoffs to leadership.

What You'll Accomplish

• Set the technical vision for the data platform: Own the long-term architectural direction for how streaming and batch systems, data models, and serving layers fit together. Make the architectural decisions that other teams and engineers build on — balancing reliability, performance, cost, and long-term maintainability across the platform.

• Build at the intersection of data and ML platform: Design the infrastructure that connects the data platform to ML workloads — feature pipelines, feature stores, and serving layers. Partner with Data Science to ensure the data platform produces ML-ready data and supports model training and inference workflows reliably.

• Raise the engineering bar across the organization: Set standards that extend beyond your immediate team — data modeling patterns, schema governance, testing practices, pipeline reliability, and code quality. Mentor senior engineers, influence engineering culture, and be the technical authority the broader R&D organization looks to on data platform decisions.

• Drive cross-organizational technical initiatives: Lead complex initiatives that span multiple teams, services, and domains. Define data contracts with upstream services, drive schema evolution strategies, and resolve systemic technical friction between data producers and consumers across the company.

• Own platform reliability and operational excellence: Drive the reliability posture of the most critical data systems. Lead improvements in observability, data quality, incident response, and cost efficiency at a platform level — making the data foundation trustworthy enough that every team in the organization can build confidently on top of it.

• Participate in the team’s weekly on-call rotation, helping ensure the reliability and timely support of the systems we build.

Basic Qualifications

• A minimum of 4+ years of hands-on data engineering experience

• Bachelor’s Degree (or equivalent) in Computer Science, Engineering, or a related technical field.

• Experience architecting data systems across batch and streaming paradigms, including technologies such as Kafka, Flink, Spark, or equivalent.

• Strong proficiency in Python and SQL, with deep experience in distributed data processing frameworks and data platform design.

• Data and ML platform crossover: You've built or contributed to ML platform infrastructure feature pipelines, feature stores, model serving, or MLOps tooling — as a natural extension of your data engineering work. You understand the ML lifecycle well enough to design data systems that serve it effectively.

• Track record of setting technical direction across an organization — driving alignment across multiple teams, making architectural decisions with broad impact, and delivering outcomes without formal authority.

• Demonstrated experience mentoring senior engineers and influencing engineering culture and standards beyond your immediate team.

Preferred Qualifications

• Data Engineering: 10+ years of hands-on data engineering experience, with a significant portion spent in platform or infrastructure roles building systems that other teams depend on.
  Built in a growth-stage environment: Your strongest work was at a mid-sized or scaling company where you had to make foundational architectural decisions — not at a large tech company where the platform was already built.

• Deep data modeling and governance instincts: You care about schema design, data contracts, and data quality as much as you care about pipeline throughput. You've driven improvements in how upstream services produce data and how downstream teams consume it.

• Product and business awareness: You connect your technical work to the problems the business is trying to solve. You understand the product use cases your platform enables and use that context to prioritize and make better architectural choices.

• Operational rigor in regulated environments: You value SLOs, incident management, and observability as first-class concerns. Experience in HIPAA, SOC 2, or similarly regulated environments is a plus.

• Experience with Databricks ecosystem: Delta Lake, MLflow, Unity Catalog — familiarity with this stack accelerates your impact.

• AI-forward engineering practices: You actively use AI-assisted development tools and see them as a force multiplier for both your own productivity and the team's.

Hinge Health Hybrid Model

We believe that remote work and in-person work have their own advantages and disadvantages, and we want to be able 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.

About Hinge Health

At Hinge Health, we’re using technology to scale and automate the delivery of healthcare, starting with musculoskeletal (MSK) conditions, which affect over 1.7 billion people worldwide. Our AI-powered, human-centered care model spans MSK, pelvic health, gastrointestinal (GI) and migraine care. With over 60 partnerships with health plans, PBMs and other ecosystem partners, Hinge Health is available to over 25 million people across more than 2,900 employers. The company is headquartered in San Francisco with additional offices in Montreal and Bangalore. Learn more at hingehealth.com.

What You’ll Love About Us

• Inclusive healthcare and benefits: On top of comprehensive medical, dental, and vision coverage, we offer employees and their family members help with gender-affirming care, tools for family and fertility planning, and travel reimbursements if healthcare isn’t available where you live.

• Planning for the future: Start saving for the future with our traditional or Roth 401k retirement plan options which include a 2% company match.

• Modern life stipends: Manage your own learning and development.

• Grow with us: Grow with us through discounted company stock through our ESPP with easy payroll deductions.

Culture & Engagement

Hinge Health is an equal opportunity employer and prohibits discrimination and harassment of any kind. We make employment decisions without regards 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, let us know by reaching out to your recruiter.

By submitting your application you are acknowledging we are using your personal data as outlined in the personnel and candidate privacy policy.

Beware of Phishing Attempts: We've noticed an increase in phishing where fraudsters impersonate employees and send fake job offers to steal sensitive information. We'll never ask for financial details during the hiring process and only use "@hingehealth.com" emails. If you receive a suspicious offer, stop communication and report it to the US FBI Internet Crime Complaint Center. To verify an email from our recruiting team, forward it to security@hingehealth.com.