Data Engineer II, AI Enablement

Avalon Healthcare Solutions is the nation’s leader in diagnostic intelligence, uniquely focused on transforming the role of diagnostic testing across the healthcare ecosystem. Our proprietary Diagnostic Insights Platform delivers evidence-based policies, curated lab networks, and real-time analytics that simplify complex diagnostics, accelerate innovation adoption, and optimize diagnostic investments. Supporting over 30 health plans and 100 million members nationwide, Avalon partners with payers and providers to ensure diagnostic testing is performed appropriately, efficiently, and at the right time. Our flexible solutions span routine and genetic testing management, automated adherence, and end-to-end diagnostics support—driving measurable value, reduced waste, and improved clinical outcomes. With unmatched scientific rigor, deep clinical expertise, and a performance-based model, Avalon is redefining how diagnostics power personalized care and healthcare value. You will be part of a team that shapes a new market and business. Most importantly, you will help Avalon to achieve its mission and improve clinical outcomes and health care affordability for the people we serve.

For more information about Avalon, please visit https://www.avalonhcs.com .

Avalon Healthcare Solutions is an Equal Opportunity Employer - Vet/Disability.

This position description is subject to change at any time. As determined by the company based upon business needs, an employee in this position may be required to perform duties and take responsibility for work other than as described in this document.

The Data Engineer, AI Enablement develops and maintains data pipelines and datasets that support analytical reporting and artificial intelligence solutions. Using a variety of data engineering methods, this role works with structured, semi-structured, and unstructured data to prepare datasets for analytics, artificial intelligence, agents, and modeling use cases. The Data Engineer, AI Enablement supports the construction and ongoing maintenance of data structures used for data-driven and AI-enabled analyses, while assessing and reporting on data quality and contributing to efficiency improvements by aligning data and AI systems with defined business and analytical goals.