Lead Engineer (Data/Integrations)
Leads architecture and implementation of healthcare payer data integrations (EDI X12, HL7/FHIR) and scalable pipelines for AI/ML systems. Requires 5+ years data engineering experience with payer data and team leadership.
About the job
Responsibilities
- Lead the architecture and implementation of data integrations with major healthcare payers, including claims feeds, utilization management authorization data, eligibility files, and provider rosters.
- Build an integration framework that makes onboarding each new payer faster and more reliable over time.
- Work across healthcare data formats and exchange methods: EDI X12 (837/835/270/271/278), SFTP, flat files, proprietary APIs, and HL7/FHIR.
- Build monitoring, reconciliation, and data quality systems, including defining requirements, test scenarios, and acceptance criteria for each integration.
- Build and scale data pipelines for AI/ML systems and analytics dashboards for behavioral and mental health quality and cost.
- Establish engineering best practices and technical foundations for future team growth.
Requirements
- 5+ years experience in data engineering, with significant time spent working with healthcare payer data (claims, UM, eligibility).
- Prior experience leading engineering teams as a tech lead or engineering manager.
- Hands-on experience integrating with health plan systems and navigating payer data formats including EDI X12, HL7, FHIR, and custom flat files.
- Strong system design skills with a track record of defining clear requirements, test scenarios, and acceptance criteria for complex data systems.
- Customer obsessed and motivated to make an impact in the healthcare space.
- A hands-on technical leader who can architect robust and extensible systems, establish best practices with AI coding tools, and ship high quality production code.
Nice-to-Haves
- Direct experience integrating with claims platforms or UM systems such as QNXT, Facets, Amisys, Jiva, CareRadius, or similar.
- Experience with behavioral health or mental health claims data (e.g., behavioral health carve-outs, BH-specific CPT codes, quality measurement).
- Built data infrastructure that powers AI/ML systems — feature pipelines, training data preparation, model monitoring.
Tech Stack
Infrastructure/Systems: AWS (ECS, Bedrock, Glue, etc.), Docker, Github Actions, Terraform Languages/Frameworks
- Backend: Python, Django, Celery / Celery Beat, django-ninja, django-tenants
- Frontend: NextJS, Typescript, Tanstack Query, Shadcn UI, Zod, Nuqs Database/Storage: PostgreSQL (AWS RDS), S3, Clickhouse Development Tools: Github, Jira, Claude Code, CoderabbitAI, Tusk
Skills
Python, Django, AWS, Postgres, ClickHouse, Edi X12, Hl7, FHIR, Docker, Terraform, Celery, Next.js, TypeScript, S3, Kubernetes
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