Full Stack Developer — Backend and Data Platform Focus

This is a hands-on individual-contributor position centered on backend engineering for a Python and SQL data platform that powers patient care-gap work for accountable care organizations and payors. Work is primarily in the data pipeline and database layers, with frontend and browser-extension tasks treated as secondary.

Role Overview

The platform reads CMS assignment files, Part A and Part B claims, Medicare eligibility data, and live hospital ADT and FHIR feeds from BigQuery. Scheduled Databricks jobs generate and manage patient care-gap items, and results are written to a PostgreSQL (Neon) database. A Next.js application and a Manifest V3 Chrome extension use the data for downstream workflows.

Correctness and auditability are core priorities. Records are append-only and fully audited, and automated runs must not overwrite work already completed by a human. Join correctness directly affects which patients are targeted, so engineering judgment focuses on blast radius, idempotency, and proof of what each script changes.

Responsibilities

• Own the opportunity-generation pipeline.
• Extend and operate a Python pipeline that transforms Medicare claims, eligibility data, and hospital ADT feeds into prioritized, workable care-gap items for a portfolio of ACOs and commercial payors.
• Write heavy SQL against BigQuery, including correct large-scale joins and aggregation.
• Query and tune multi-million-row datasets, including assignment files, Part A and B claims and line items, eligibility responses, FHIR encounter and ADT feeds, plus deduplication and late-arriving-data handling.
• Build and run Databricks jobs.
• Package pipeline scripts as scheduled jobs (Python script tasks, cluster and dependency configuration, DBFS, secret resolution), and run controlled one-off production jobs for remediation.
• Design and migrate PostgreSQL and manage schema, indexing, and query performance on Neon.
• Use versioned SQL migrations, staging and historical mirror tables, JSONB metadata columns, read-replica usage for batch reads, and database-enforced immutability.
• Ensure generators are idempotent and safe to re-run.
• Implement deterministic record IDs, upsert paths that preserve human work, and supersede or carry-forward logic across reporting periods and status lifecycles.
• Maintain an append-only audit log behind every state change.
• Protect production data through dry-run-then-commit scripts, volume abort thresholds, environment guards, backups, and rollback artifacts, plus remediation that repairs affected rows without destroying history.
• Extend automated data-quality monitoring with invariant and drift checks on live production data, alerting, and anomaly investigation through written root cause documentation.
• Test pipeline logic with pytest, ephemeral PostgreSQL fixtures, frozen clocks, and pre-commit gates to prevent regressions in nightly runs.
• Publish and defend data contracts by documenting record contents and field-level constraints so the application team can build against them, coordinating breaking changes across repositories.
• Move into application-layer work when needed, including Next.js, React, and TypeScript surfaces, REST APIs with role-based access control and HIPAA-compliant audit logging, Firebase Cloud Functions and other serverless services, and the Manifest V3 Chrome extension integrating with web-based EHR systems.
• Debug end-to-end by tracing incorrect application outputs back through PostgreSQL, the generator that produced the records, the Databricks run source, and upstream BigQuery feeds.
• Collaborate across a small team and two repositories, including code review and coordination with application and clinical-operations stakeholders on data needs.
• Handle Protected Health Information responsibly with attention to performance, reliability, scalability, and security across every layer.

Required Qualifications

• 4+ years of professional software development experience.
• Strong Python for production data work, including pandas, with scheduled batch jobs and command-line tools (not notebook-only experience).
• Advanced SQL including CTEs, window functions, large multi-table joins, aggregation correctness, and query optimization on large datasets.
• Strong PostgreSQL skills: schema design, indexing, EXPLAIN-driven tuning, transactions, upserts, and JSONB.
• Experience writing and applying SQL migrations against a live production PostgreSQL database.
• Experience with a cloud data warehouse, with BigQuery strongly preferred, including partitioning and query cost/performance awareness.
• Experience building and operating scheduled data pipelines, including idempotent re-runs, backfills, duplicate and late-arriving sources, and reconciliation between systems.
• Experience with Databricks or Spark, including jobs, clusters, dependency management, secrets, and DBFS, or a comparable managed job platform.
• Operational judgment with production data: dry runs, blast-radius estimates, auditability, and rollback planning before writing changes.
• Ability to debug complex issues across warehouse, batch jobs, databases, and application components.
• Working ability in TypeScript with React and Next.js to ship frontend or API changes when required.
• Experience designing and building REST APIs.
• Comfort with Git and pull-request workflows, including giving and receiving code review.
• Clear written communication to explain data defects, causes, and fixes in actionable documentation.
• Strong problem-solving skills and attention to detail.

Technology Stack

• Python, SQL, pandas
• Databricks, BigQuery, Spark
• PostgreSQL, Neon, JSONB
• Next.js, React, TypeScript
• Chrome extension (Manifest V3)
• FHIR
• REST APIs
• Firebase Cloud Functions, serverless services
• pytest, Git

Role Level

This is a hands-on individual-contributor role and is not suited for entry level or new graduate candidates.

Location and Compensation

• Location: Remote
• Salary: USD 70,000 per year
• Minimum Experience: 4 years

Benefits

• 401(k) 6% Match
• Bereavement leave
• Dental insurance
• Family leave
• Flexible spending account
• Health insurance
• Health savings account
• Life insurance
• Paid parental leave
• Parental leave
• Vision insurance
• 401(k) matching
• Employee discount
• Paid time off
• Professional development assistance
• Referral program
• Tuition reimbursement

Nice to Have

• Healthcare data experience: CMS Part A and Part B claims, Medicare beneficiary identifiers (MBI), HL7 v2 ADT feeds, FHIR, or eligibility services such as HETS.
• Value-based care domain knowledge: MSSP or ACO programs, risk adjustment and HCC coding, Annual Wellness Visits, transitional care management, or quality measures.
• Experience with healthcare technology, including EHR integrations (eClinicalWorks, Athena, Epic, or similar).
• Experience with Firebase Cloud Functions, Firestore, and Google Cloud Platform (IAM, service accounts, Secret Manager).
• Experience with Prisma ORM.
• Experience developing Chrome Extensions using Manifest V3.
• Data quality and observability work: anomaly detection, invariant checks, drift monitoring, and alerting.
• Experience integrating LLMs such as Claude, OpenAI, or Gemini.
• Experience building speech to text or AI powered workflows.
• Experience building internal operator tooling for running, configuring, and monitoring pipeline jobs.
• Familiarity with modern Python tooling (uv, pytest plugins, pre-commit hook managers) and multi-package repositories.
• Familiarity with Rust for performance-sensitive components, browser tooling, or systems programming.
• Familiarity with HIPAA compliance and secure handling of healthcare data.

Application Questions

• Have you worked on software handling PHI or in a HIPAA-regulated environment?
• Describe a production data pipeline you owned end to end: the data sources, the schedule, the volume, and what you did the first time it produced bad data.
• What is your hands-on experience with BigQuery or another cloud data warehouse, and with Databricks or Spark?
• How comfortable are you writing and applying SQL migrations against a live production PostgreSQL database, and what precautions do you take?