Senior Backend Engineer, Provider Directory
Remote (US) · Full-time
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.
Position Overview
b.well's Provider Directory is the data backbone that helps millions of people find the right doctor, connect to their health records, and take control of their care. We ingest provider data from national registries, EHR systems, health networks, and partner feeds — normalize it into a FHIR-native graph of practitioners, organizations, and their relationships — and serve it at scale through search and export channels that power our consumer apps and partners like Google.
Provider data in healthcare is notoriously fragmented — scattered across hundreds of sources, riddled with duplicates, stale records, and conflicting identities. No single source tells the truth. That's the problem you'll solve.
You'll own the ingestion, data quality, and operational layer of the provider directory — from raw source files landing in S3 to refined FHIR resources flowing into search indices. You design these pipelines, build them, monitor them, and make them better. AI-driven development isn't a buzzword here — it's how we work. You'll use AI tools fluently in your own engineering workflow, and you'll build AI-powered tooling that analyzes data quality, scores confidence, resolves entities, and infers relationships that no single source provides.
This is a hands-on, high-ownership role. The hard problems are about data quality at national scale, fault-tolerant distributed processing, and making sense of real-world healthcare data that resists clean modeling.
What you'll do:
- Design and build scalable data ingestion pipelines that onboard new provider data sources — EHR brand files, national registries, partner feeds — and transform them into standardized FHIR resources (Practitioner, Organization, PractitionerRole, Endpoint, Location).
- Own data quality end-to-end: define validation rules, confidence scores, and quality thresholds; build automated monitoring and alerting; identify and resolve issues before they reach users.
- Build AI-powered data tooling — entity resolution across duplicate provider records, practitioner-to-organization relationship inference, specialty classification from unstructured data, and quality scoring that quantifies how much you trust a record.
- Operate and evolve existing Spark-based pipelines orchestrated by Prefect on AWS, improving reliability, observability, and onboarding speed so new data sources go from raw files to searchable records in days, not weeks.
- Establish data governance standards for the provider directory: schemas, staging processes, and refinement workflows that turn messy inputs into trustworthy outputs.
- Partner with Analytics to build data quality dashboards and reporting. Partner with Product and Business teams to prioritize which data sources and quality improvements have the highest user impact.
- Lead incident response when data issues arise — stale records, broken pipelines, source regressions — and build the observability to catch problems before users do.
- Ensure security, compliance (HIPAA, HITECH), and scalability are built into data and API solutions from day one.
What we're looking for:
- A strong backend engineer who builds and operates data-intensive systems at scale. You've dealt with large datasets, complex ETL, and the operational reality of keeping pipelines healthy in production. 5+ years of experience in this kind of work.
- Deep Python proficiency — it's the primary language here. You're comfortable with Spark or Databricks for distributed data processing, and with workflow orchestration tools like Prefect or Airflow.
- Sound data and storage instincts: you think clearly about data modeling, schema design, validation strategies, and the trade-offs between batch and streaming approaches. You've worked with both relational and document databases (we run MongoDB and OpenSearch).
- You use AI tools like Claude as a natural part of your development workflow — not as a novelty, but as a force multiplier. You can also see where AI fits into data tooling (entity resolution, classification, enrichment) and build those integrations.
- Cloud-native fundamentals: AWS (S3, ECS/EKS, Lambda), Docker, Kubernetes, CI/CD with GitHub Actions. You know how to deploy and operate services, not just write them.
- Strong ownership instincts. You don't wait for someone to hand you a ticket with a solution attached. You see a data quality gap, you investigate it, you propose a fix, you ship it.
- Clear communication. You work with data scientists, product managers, and business stakeholders — not just other engineers. You can explain a data problem and a proposed solution to any of them.
- Healthcare or FHIR experience is a strong plus and something you'll go deep on here. If you haven't worked in the domain yet, that's fine — it's learnable, the standards are well-documented, and we'll get you there. What we can't teach is engineering judgment and the instinct to build things well.
What success looks like after 12 months:
- New data sources onboard in days, not weeks — with clear, repeatable processes and minimal hand-holding.
- Data quality is measured, monitored, and visibly improving. You can point to dashboards that prove it and alerting that catches regressions before users notice.
- Pipelines run reliably and observably. When something breaks, you know about it before anyone files a ticket — and the blast radius is contained.
- AI-powered tooling is delivering real value: better entity resolution, smarter relationship inference, higher-confidence records.
- You've established data governance standards that the team and cross-functional partners trust and build on.
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