Data Engineer

Description

The Data Engineer is responsible for designing, developing, integrating, and maintaining the data infrastructure necessary to support clinical, operational, financial, quality, and strategic initiatives across Pinehurst Surgical Clinic. This position works across EHR platforms, clinical applications, business systems, interfaces, reporting platforms, and emerging technologies to ensure organizational data is reliable, accessible, secure, and actionable.

The role develops automated data pipelines and integrations, improves reporting and analytics capabilities, reduces manual data-processing workflows, and establishes scalable data architecture to support future analytics, artificial intelligence, and automation initiatives. The Data Engineer collaborates with Health Information Systems, clinical and operational leadership, vendors, analysts, and other stakeholders to translate organizational needs into technical solutions.

• Design, develop, maintain, and monitor ETL/ELT workflows and automated data pipelines between clinical, operational, financial, and third-party systems.
• Develop integrations using APIs, databases, flat files, and healthcare interoperability standards including HL7, FHIR, C-CDA, and related technologies.
• Extract, normalize, transform, reconcile, and validate data from multiple healthcare applications and data sources.
• Design scalable data structures, relational models, reporting datasets, and data marts to support analytics and organizational reporting.
• Develop processes for historical data extraction, conversion, archival, and migration associated with application transitions.
• Troubleshoot data integration, synchronization, mapping, and data-quality issues across systems.

• Support data and integration requirements associated with Epic Community Connect.
• Assist with source-to-target mapping, historical data conversion, interface validation, reporting requirements, and reconciliation between legacy and future-state systems.
• Develop technical solutions to preserve access to historical clinical and operational information as systems are transitioned or retired.
• Collaborate with clinical, operational, and technical teams to ensure data remains accurate and clinically meaningful throughout system implementations and migrations.

• Develop reliable datasets and automated processes supporting clinical quality, MIPS, operational, financial, and executive reporting.
• Build data-validation and reconciliation processes that improve the completeness and accuracy of regulatory and quality reporting.
• Partner with operational and clinical stakeholders to translate business requirements into measurable KPIs, data models, dashboards, and reporting solutions.
• Support automated identification of missing or incomplete clinical information and development of workflows that allow staff to efficiently address identified deficiencies.
• Develop and maintain integrations and datasets supporting multiple business intelligence platforms.

• Identify manual, repetitive, or data-intensive workflows that can be automated.
• Develop scripts, applications, API integrations, and workflow automation solutions to improve efficiency and data accuracy.
• Use Python, SQL, Microsoft Power Platform, APIs, and related tools to automate data movement and business processes.
• Work with operational teams to measure the effectiveness of automated workflows and identify opportunities for continued improvement.

• Develop and maintain the data infrastructure and integrations necessary to support approved artificial intelligence and machine-learning…