The Phia Group, LLC

• Design, build, and maintain applications supporting claim recovery and healthcare-related workflows • Translate complex business processes into scalable, maintainable, and efficient system functionality • Implement solutions involving financial logic, including balances, allocations, and reconciliation • Ensure code is production-ready, well-tested, and complete prior to QA • Leverage AI tools (e.g., GPT, Claude, Copilot, or similar) to: Accelerate code development and refactoring, Assist with debugging and root cause analysis, Generate tests, documentation, and validation logic • Identify and implement opportunities to automate repetitive development and operational workflows • Continuously evaluate and adopt new AI tools to improve engineering efficiency • Work extensively with relational databases and large datasets • Write and optimize complex SQL queries, stored procedures, and data transformations • Ensure data integrity, reconciliation accuracy, and system performance at scale • Troubleshoot and resolve data discrepancies across systems • Build and maintain integrations across internal and external systems via APIs and data pipelines • Design systems that support multi-step workflows and conditional business logic • Ensure reliable data flow, error handling, and system resiliency • Collaborate closely with Operations and business stakeholders to align systems with real-world workflows • Participate in code reviews, design discussions, and technical decision-making • Mentor developers and contribute to improving team standards and delivery practices • Drive a culture of ownership, accountability, and first-time-right delivery

• supporting the development, maintenance, and optimization of data pipelines and analytics-ready datasets • collaborating across multiple teams and stakeholders to solve complex problems and support data-driven initiatives • build, maintain, and optimize data pipelines utilizing Azure Data Factory • implement monitoring, alerts, and testing of data pipeline performance, data quality metrics, and lineage • troubleshoot data issues and perform root cause analysis • collaborate with analysts, stakeholders, and product owners to translate business needs into data requirements • support deployment and operationalization of machine learning models • continually improve ongoing reporting and analytics