Senior Data Engineer, Arbital Health

• If you are excited about building a new healthcare data and analytics platform to support Value Based Care (VBC) that will help reduce the cost of healthcare in the US, and the following matches your skills, experience, and interests:
• Programming in Python, Spark or other big data technologies
• Development and deployment of a data-intensive product on AWS and Databricks
• AI-native development with Cursor/Claude/Copilot
• 5+ years building data-intensive SaaS platforms (L5: 8+ years with technical leadership)
• Deep, hands-on expertise with Spark and distributed data processing
• Strong SQL and data modeling / warehouse design (dimensional modeling, Delta / Lakehouse)
• Proven track record scaling a product to an enterprise level
• Experience with orchestration (Airflow), IaC (Terraform), and CI/CD for data
• Experience with data-quality / testing frameworks such as dbt tests or Great Expectations
• Ability to quickly understand complex modeling workflows and the business need driving them
• Ships high-caliber, well-tested code with strong attention to detail
• Experience with healthcare data (claims, eligibility) and handling PHI / PII under HIPAA
• Thrives under minimal supervision in a rapidly changing, ambiguous start-up environment

Desirable

• We’ve built a production data pipeline that ingests, enriches, aggregates, and summarizes healthcare financial data so it can be easily utilized in our AI and web tools
• As we're continuing to scale in both data size and complexity, we are looking for a senior data engineer to help us enhance and scale this core part of our platform
• Scale Arbital's healthcare data pipelines and lakehouse on AWS and Databricks, and own the underlying architecture
• Implement and scale actuarially sound healthcare financial calculations in Spark
• Build and maintain orchestration (Airflow) and CI/CD so enrichment and aggregation workflows are reliable, observable, and reproducible
• Own data quality, integrity, privacy, security, and HIPAA compliance through automated testing and quality-control procedures
• Collaborate with actuarial and delivery teams that primarily work in Python and R
• Partner with data scientists to deploy and monitor machine learning models in production
• Lead technical design reviews and contribute to platform-wide architecture decisions
• Establish data observability, lineage, and SLAs, and tune Spark/Databricks jobs for performance and cost
• Raise the engineering bar through code review, mentorship, and setting data-engineering standards across the team
• Tools we use include:
• AI Tools: Cursor, Claude, Gemini, Databricks Genie
• Core Tools: Python, R, SQL, Next.js, React, TypeScript, Tailwind CSS
• Infrastructure: AWS, Databricks, Airflow, Terraform
• Version Control: GitHub
• Team Planning: Jira, Confluence
• The opportunity to build a critical software platform that accelerates the American healthcare system's transition to value-based care