Data Engineer, Arbital Health

• We're looking for an ambitious data scientist or data engineer to help us enhance and scale this core part of our platform
• 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 ( R is a plus)
• Development and deployment of a data intensive product on a SaaS platform
• Statistical or financial calculations, preferably with healthcare data
• Experience in data-intensive, SaaS platform development projects
• Demonstrated ability to scale new platforms into enterprise products
• Demonstrated history of being able to quickly understand complex modeling workflows and the big picture driving the need for them
• Ability to ship extremely high-caliber code and build exceptional products
• High level of attention to detail
• Ability to perform under minimal supervision with accountability for specific objectives and work in a rapidly changing, ambiguous start-up environment
• Passionate about improving and innovating

Desirable

• We’ve built the initial production data pipelines that ingest, enrich, aggregate, and summarize healthcare financial data so it can be easily visualized in our web app by our customers
• In addition to sophisticated financial calculations, this code matches patients against a benchmark population so that our clients can understand how their patients are doing in comparison to similar individuals
• Help build Arbital's highly scalable data pipelines and warehouses using platforms like AWS and Databricks
• Automate and maintain data workflows for enrichment, aggregation, and quality assurance
• Ensure data accuracy, integrity, privacy, security, and compliance through automated quality control procedures
• Work on a team with full-stack engineers and actuarial coders to implement end-to-end solutions for contract adjudication
• Implement and scale actuarially sound healthcare financial calculations
• Collaborate with actuarial science and delivery teams that primarily work in R and Excel
• Partner with data scientists to deploy machine learning models in production
• Contribute to the design, implementation, and overall development of our products
• Drive innovation and deliver valuable features for our customers
• Tools we use include
• Infrastructure: AWS, GCP, Databricks, Airflow, Sigma, Datadog
• Core Tools: Python, R, SQL, Next.js, React, TypeScript, Tailwind CSS
• IDE: Cursor, VS Code, and R Studio
• Version Control: GitHub
• Team Planning: Jira, Confluence, Figma