Staff Engineer, Healthcare Data

**Staff Engineer, Healthcare Data** Individual Contributor — Staff Level Health Data Platform · FHIR at Scale **Company Overview** b.well is solving healthcare's fragmentation problem with our FHIR-based health data management platform. The platform connects data from EHRs, wearables, portals, and other sources, while our intelligence engine personalizes the consumer experience. By simplifying the complex healthcare ecosystem, we make it easy and convenient for consumers to engage and take action—whether it's scheduling care, setting reminders, accessing health data, and more. For our clients, this means better health outcomes, operational efficiency, and stronger consumer engagement. **The Role** You'll set the technical direction for how b.well ingests, transforms, and delivers healthcare data at scale on our FHIR-based platform—owning our hardest problems (large-scale pipeline performance, data quality and de-duplication, terminology normalization, and reliable delivery of standards-conformant FHIR) and turning each solution into reusable frameworks the whole team builds on. This is an AI-native role: you'll pair first-principles engineering rigor with modern agentic tooling—coding agents such as Claude Code and reusable AI skills—to ship faster and turn raw health data into trustworthy, reasoning-ready health data intelligence. **What You'll Do:** Work AI-Native & Set the Bar - Make coding agents (e.g., Claude Code) and reusable AI skills a first-class part of how the team engineers—shipping faster by automating the repetitive and amplifying the hard parts - Bring the rigor to know when to trust an agent, when a deterministic rule-based approach wins, and how to keep AI-generated code correct, tested, and PHI-safe - Package engineering and data-quality judgment into reusable tooling the whole team applies consistently Build & Scale Healthcare Data Solutions - Own the end-to-end architecture of large-scale pipelines that transform raw source data into clean, standards-conformant FHIR - Diagnose and eliminate performance and scaling bottlenecks in distributed processing so jobs run faster and more cost-efficiently - Set standards for data quality, de-duplication, patient/record linking, and terminology normalization; drive reliable, idempotent delivery at production volume - Build automated testing and validation for data quality and FHIR conformance Lead, Secure & Mentor - Set technical direction across ingestion, transformation, and delivery, and define the frameworks and standards that accelerate the team - Champion PHI-safe engineering and uphold HIPAA, 21st Century Cures Act, and ONC/CMS compliance across the platform - Mentor senior and mid-level engineers, and represent b.well in relevant standards and industry discussions **Required Experience:** Must-Have - 8+ years in data engineering, health IT, or healthcare interoperability—with a Staff-level track record of owning systems, not just tasks - Proven production experience building large-scale health-data pipelines and owning a complex platform end-to-end, including its hardest performance and correctness problems - Experience defining frameworks and standards that measurably accelerated a team - AI-native engineer: you already ship with coding agents (e.g., Claude Code) and reusable AI skills, and can raise the whole team's leverage without compromising engineering rigor Technical Skills - Distributed data engineering: Apache Spark / PySpark at scale (partitioning, performance and memory tuning); lakehouse tooling such as Databricks and Delta Lake; schema evolution; batch and streaming pipelines - Pipelines & languages: ETL/ELT, CDC, incremental/delta and idempotent processing; strong Python (required) and SQL; Git, CI/CD, and automated testing - FHIR & standards: HL7 FHIR R4 (resources, profiles, Bundles), US Core / USCDI, and HL7 v2.x / C-CDA to FHIR conversion - Terminologies & payer data: SNOMED CT, LOINC, RxNorm, ICD-10, CPT, CVX and crosswalks; claims, cover…