Data Engineer

The Data Engineer plays a critical role in designing, building, and maintaining the enterprise data infrastructure that powers clinical, operational, and strategic decision-making across Mountain Park Health Center.

This position develops reliable data pipelines, data warehouses, data lakes, and Dataverse solutions that transform data from multiple sources into accurate, accessible, and actionable information.

The Data Engineer will collaborate with other IT team members and stakeholders to review, modernize, implement, and maintain data architecture, artificial intelligence, and machine learning based innovations in a healthcare setting to strengthen interoperability in a community focused healthcare organization.

• Designs, builds, implements, and maintains scalable data pipelines and Extract Transform Load (ETL)/Extract Load Transform (ELT) processes that integrate data from diverse internal and external systems to support enterprise-wide analytics.
• Builds and optimizes data pipelines that prepare, transform, and deliver high-quality datasets to support predictive modeling use cases such as patient no-show risk, care gaps, and population health insights.
• Develops data workflows that support both real-time and scheduled prediction use cases, ensuring the timely delivery of predictive insights for operational decision-making (e.g., scheduling or outreach).
• Creates and establishes data quality standards, validation frameworks, and monitoring processes while advancing data governance practices, including data definitions and metadata management.
• Enables interoperability and healthcare data exchange by developing and managing integrations using healthcare standards such as: Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR), and modern data exchange methods: Application Programming Interface (APIs), Secure File Transfer Protocol (SFTP), flat files to ensure seamless and accurate data flow across systems.
• Partners with clinical, operational, and business stakeholders to identify predictive analytics use cases (e.g., no-show prediction, risk stratification, utilization forecasting) and ensures the necessary data architecture is in place to drive improved patient outcomes and organizational performance.