5+ years experience in data engineering specifically within healthcare payers
Proven leadership experience managing engineering teams
Hands-on expertise with health plan integration and payer data formats
Strong system design skills for large-scale data systems
Customer-focused mindset with commitment to healthcare impact
Responsibilities
Lead architecture and implementation of data integrations with major payers
Develop an integration framework for efficient onboarding of new payers
Handle various healthcare data formats and exchange methods
Build data quality and monitoring systems for integrations
Create scalable data pipelines for AI/ML applications
Benefits
Flexible hybrid work policy with remote-first culture
Unlimited vacation policy
Paid parental leave
Comprehensive medical, dental, and vision insurance
401(k) with matching options
Significant equity as an early employee
Mentorship from experienced founders
Regular team events and offsites
Full Job Description
The Role
We're seeking an experienced data engineering leader 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 3 times per 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
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.
Development Tools: Github, Jira, Claude Code, CoderabbitAI, Tusk
What we're looking for:
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
Experience integrating (or willing to learn) 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
Bonus points if you have (not required):
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
Benefits and Perks
Flexible hybrid arrangement: 3 days/week at San Francisco office (Financial District), remote-first culture
Unlimited vacation policy
Paid parental leave
Medical, dental, and vision insurance
Pre-tax commuter benefits
401(k)
Significant equity as an early employee
Direct mentorship from experienced founders
Ground-floor opportunity to help build a team and culture
