Hiring: Senior Staff Data Engineer - Data & ML Platform Company: Hinge Health Location: San Francisco, CA Job Posted Time: 2026-09-10 11:43:38 Employment Type: Remote Target Skills & Keywords : AWS, Databricks, Delta Lake, Feature Store, Flink, HIPAA, Kafka, MLOps, MLflow, Machine Learning, Python, R, SOC 2, SQL, Spark, dbt About the job Experience: •4+ years of hands-on data engineering experience Required Skills: •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. •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. 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. •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. •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. •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. Compensation: •$240,000 - $360,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!