Senior Engineer, Healthcare Data Individual Contributor — Senior 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 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-conformant 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. What You'll Do: Work AI-Native & Ship Faster - 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, Onboard & Operate - 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 Keep Production Healthy & Efficient - 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 Required Experience: 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 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, coverage, and eligibility data models What Makes You Successful: - 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 The target salary range for this position is $160,000 - $190,000 annually and is part of a competitive total rewards package including stock options, benefits, and incentive pay for eligible roles. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually at minimum to ensure competitive and fair pay. Data shows that women, people of color, and other underrepresented groups may be less likely to apply for jobs unless they believe they are a perfect match. But b.well holds diversity amongst its key values, and we have a strong commitment to building our workforce and products through that lens. You don't have to check every box in this job description to be a great fit for the role! If you're excited about this position and the prospect of working for b.well, please apply. If it turns out this role isn't for you, there may be other openings that could align with your experience and expertise! We are committed to an inclusive and diverse b.well. We are an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran, genetic information, marital status or any other legally protected status