Arbital Health operates an AI-powered adjudication platform and actuarial team that centralizes fragmented data, automates outcomes-based contract reconciliation, and delivers risk dashboards, insights for payers, providers, digital health.
Arbital Health is a rapidly growing healthcare technology and actuarial leader that centralizes, measures, and adjudicates value-based care contracts at scale. We enable payers and providers to design, measure, and execute value-based agreements with greater transparency, efficiency, and financial predictability. We invest in hiring high potential and humble individuals who thrive in fast-paced environments and can rapidly grow their responsibilities as we continue to accelerate our growth. We were co-founded by Brian Overstreet and Travis May (founder & former CEO of LiveRamp and Datavant, the two biggest data companies of the last 20 years), and are backed by Transformation Capital, Valtruis and other leading investors. In our first 2 years, Arbital Health has established itself as a trusted partner for over 40 payers, providers, and other stakeholders looking to navigate the complexities of risk-based contracting. 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. Is this role right for you? 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 Responsibilities: 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 Requirements: 5+ years building data-intensive SaaS platforms 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 Our team works in a hybrid model from the San Francisco Bay Area. We will prioritize candidates who are able to work 2 days per week from our office, and we will consider highly qualified remote candidates who can travel quarterly to the San Francisco office. Nice to have's: Startup experience is highly preferred Extensive experience with Airflow, Databricks, Python, and AWS or GCP Streaming / near-real-time data such as Structured Streaming or Kafka Databricks certification (Data Engineer Associate or Professional) 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