Senior Engineer, Healthcare Data

Position:
Senior Engineer, Healthcare Data

Location:

Remote

Job

# of Openings: 1

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.

A hands-on role for a strong, broad engineer who owns healthcare data end-to-end—from ingesting a new source, through transforming and de-duplicating the data, to reliably delivering clean, standards-conform ant FHIR. You're comfortable shipping new services, pipelines, debugging production issues, and tuning jobs to run faster and more cost-efficiently, and you're trusted to take a source or a problem from zero to production.

This is an AI-native role: you'll use modern agentic tooling - coding agents such as Claude Code and reusable AI skills to ship faster, paired with a strong engineering mindset that keeps the work correct and PHI-safe, turning raw source data into trustworthy health data intelligence.

• Reach for coding agents (e.g., Claude Code) and reusable AI skills to accelerate the repetitive- mappings, tests, boilerplate- so your judgment goes to the hard parts
• Know when a deterministic rule-based approach beats an LLM call for cost, speed, and reliability, and keep AI-assisted code tested and PHI-safe
• Contribute reusable skills others can build on

• Build and maintain pipelines that transform source data into standards-conform ant FHIR, with enrichment, record/patient linking, terminology normalization, and de-duplication
• Ingest new data sources (file parsing, decryption, mapping) and integrate provider/reference data such as national provider registries
• Debug and stabilize failing workflows and source connections so downstream users get their data

• Add resilience (e.g., retry logic) and tune throughput so jobs land reliably
• Improve cost and performance- choosing batch vs. streaming appropriately and replacing costly steps with efficient rule-based logic where it fits
• Root-cause data issues that surface downstream back to the pipeline; write unit and integration tests; handle PHI and encrypted files safely

Must-Have

• 6+ years in data engineering, microservices, health IT, or healthcare interoperability
• Proven experience owning data pipelines end-to-end in production (including on-call/support), shipping across ingestion, transformation, and delivery
• Comfortable being the person who takes a new source from zero to production
• AI-native engineer: you already use coding agents (e.g., Claude Code) and reusable AI skills to ship faster, with the judgment to keep AI-assisted work correct, tested, and PHI-safe

• 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