Staff Data Engineer

About NexHealth Our healthcare system remains frustratingly analog. When you live in a world of one-tap car rides, instant meal delivery, and unlimited streaming, why do you still have to call to schedule a doctor’s appointment and fill out a clipboard in the waiting room? NexHealth’s mission is to accelerate innovation in healthcare by connecting patients, providers, and developers. We’re building the infrastructure layer for modern healthcare, connecting thousands of fragmented, on-premise, and closed EHR systems into a single, modern platform that powers software, APIs, payments, and patient experiences across the ecosystem. Founded: 2017 Headquarters: San Francisco, CA Funding: $177M Series C Employees: 200+ Trusted by tens of thousands of providers and hundreds of health-tech developers — forging the infrastructure layer that modern healthcare needs About The Role As our Staff Data Engineer, you will own and evolve the foundational data systems that power NexHealth’s analytics and unlock the next generation of customer-facing insights. This is a hands-on, high-ownership role that combines architectural influence with deep execution. We need to expand our data capabilities for scale and improve our daily operations and enable self-service. You will drive the transformation of our data infrastructure. You will design data pipelines, refactor the warehouse architect [... source excerpt omitted ...] at enable trusted analytics across the company. This work directly supports our mission to become the API for healthcare. With access to a rich and growing corpus of medical data, you will help NexHealth become a leader in data strategy, intelligence, and customer enablement. This role is ideal for a Staff-level engineer who has seen and built great data platforms, is not afraid of legacy, and thrives in environments where the vision is clear, the systems are evolving, and the company is depending on data to unlock its next stage of growth. What You’ll Do Own the Data Platform Lead the architectural evolution of our data warehouse, modeling layer, and pipeline structure Refa [... source excerpt omitted ...] s to support fast, self-serve access and shared understanding across teams Build reliable ingestion and transformation systems that abstract over messy or fragmented inputs Take responsibility for the structure, reliability, and extensibility of our warehouse and modeling layers Replace brittle vendor-based pipelines with scalable, maintainable ingestion and transformation infrastructure Build in observability, alerting, and validation to ensure the platform can scale with confidence Drive Strategic Data Initiatives Collaborate closely with Staff-level analysts, engineers, and data consumers to unblock their workflows and enable data insights. Partner with software engineers to