Note: The job is a remote job and is open to candidates in USA. b.well Connected Health is addressing healthcare's fragmentation through their FHIR-based health data management platform. The Staff Engineer, Healthcare Data will lead the technical direction for ingesting, transforming, and delivering healthcare data at scale, ensuring high standards of data quality and compliance while mentoring other engineers.
Responsibilities
- 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
- 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
- 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
Skills
- 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
- 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, coverage, and eligibility data models
- Compliance: HIPAA, 21st Century Cures Act, ONC certification, and CMS Interoperability Rule
- Large-scale FHIR data modeling; patient matching / MPI and clinical data-quality profiling
- Payer data exchange (CARIN Blue Button, Da Vinci) and lab/pharmacy data
- LLMs/agents for clinical data extraction, normalization, and terminology mapping; open-source healthcare or HL7 working-group contributions
Benefits
- Stock options
- Benefits
- Incentive pay for eligible roles
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