Lead Engineer (Data/Integrations)

About the role

We're seeking an experienced data engineering leader with deep healthcare payer domain expertise who is motivated to meaningfully improve the way healthcare is administered in the country. You'll own the architecture and implementation of our integrations with claims and utilization management systems at the nation's largest health plans. As an early team member, you'll build the data infrastructure that powers the entire Onos platform and establish the best practices for a growing data engineering team. This role is a hybrid role based in San Francisco, where you'll be expected to work at our office in person 2-3 times a week. What you'll be doing at Onos: 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 our 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 Technical Challenges At Onos: Build a payer integration framework that makes each new national and regional health plan onboarding faster and more reliable than the last Develop fault-tolerant data pipelines that process millions of claims records while maintaining strict data quality, security, and compliance requirements Design normalization layers that translate varied payer data formats into a unified data model for AI/ML and analytics Architect robust monitoring and alerting systems that detect data quality issues, feed disruptions, and schema drift before they impact downstream systems Establish best practices for using AI tooling across the engineering workflow to ship faster without sacrificing quality Tech Stack: At Onos, we work with a modern tech stack where we continuously evaluate and adopt cutting-edge technologies as we scale. 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