Senior 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.

• 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.

• A minimum of 4-6+ 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.

• Demonstrat…