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Staff Engineer, Healthcare Data

b.well Connected Health
Remote, USA
Full-time
Senior Level
Remote
USD 180000-220000 yearly
Discovered 1 months ago

Full Job Posting

  • 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
  • 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
  • Deep curiosity and passion for hard healthcare-data problems, with relentless debugging instincts across multi-stage pipelines
  • Uncompromising commitment to data quality, patient safety, and PHI/consent security
  • Self-directed—you drive ambiguous problems to a production fix and communicate clearly to technical and non-technical audiences
  • AI-native by default: fluent with coding agents (e.g., Claude Code) and reusable AI skills, shipping faster without sacrificing rigor
  • Strong engineering mindset: first-principles thinking, deep ownership, correctness over cleverness
  • Treats health data as intelligence: turns messy source data into clean, coded, de-duplicated, reasoning-ready records

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