Senior Engineer- Healthcare Data

b.well Connected Health is solving healthcare fragmentation through a FHIR-based health data management platform that connects data from EHRs, wearables, portals, and other sources. The Senior Engineer, Healthcare Data owns healthcare data end-to-end, building and operating pipelines that ingest, transform, de-duplicate, and deliver standards-conformant FHIR data while using AI-native tools safely and effectively.

Responsibilities:

• 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-conformant 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

Requirements:

• 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