Data Engineer

Pinehurst Surgical Clinic PA•Pinehurst, NC

13d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in computer science, Information Systems, Data Science, Computer Engineering, Information Technology, or a related discipline; an equivalent combination of education and relevant experience may be considered.
Three or more years of experience in data engineering, database development, ETL development, systems integration, business intelligence, application development, or a related technical discipline.
Strong SQL skills with experience querying, transforming, validating, and optimizing relational data.
Experience with Python or a comparable programming/scripting language.
Experience designing or supporting ETL/ELT workflows and automated data pipelines.
Experience integrating systems using REST APIs or similar integration technologies.
Experience with relational databases such as Microsoft SQL Server or comparable platforms.
Demonstrated ability to analyze complex problems, work independently, and translate operational requirements into technical solutions.

Nice To Haves

Experience working with healthcare clinical, operational, or claims data.
Experience with Epic, TouchWorks, or other EHR/EMR platforms.
Knowledge of healthcare interoperability standards including HL7, FHIR, C-CDA, and healthcare terminology/code sets.
Experience with healthcare data migration, system conversion, or legacy-system archival.
Experience with Microsoft Azure and/or other cloud data platforms.
Experience with Power BI, Power Automate, Power Apps, Logic Apps, or comparable automation and analytics technologies.
Experience supporting MIPS, clinical quality measures, registries, or other regulatory healthcare reporting.
Experience developing data architecture supporting artificial intelligence or machine-learning applications.
Familiarity with generative AI platforms, large language model APIs, prompt-based workflows, retrieval-augmented generation, or similar AI technologies.
Experience implementing workflow automation using APIs, Python, Microsoft Power Platform, or AI-enabled services.
Understanding of HIPAA, PHI security, data governance, auditability, and healthcare regulatory requirements.

Responsibilities

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 applications.
Integrate approved AI services and APIs with organizational systems and workflows when appropriate.
Prepare, structure, validate, and govern organizational data used by AI-enabled applications.
Assist with development of automated workflows incorporating generative AI, natural-language processing, predictive analytics, or other emerging technologies.
Evaluate emerging data, automation, and AI technologies and recommend appropriate use cases based on organizational needs.
Establish appropriate controls for AI-enabled workflows involving PHI or other sensitive organizational information.
Assist with measurement of AI initiatives, including utilization, workflow impact, accuracy, efficiency, and other defined performance indicators.
Maintain data architecture, mapping, interface, pipeline, and technical-process documentation.
Develop and implement data-quality standards, validation rules, monitoring, and exception reporting.
Support organizational data governance including data ownership, definitions, lineage, access, retention, and appropriate use.
Ensure data solutions comply with HIPAA, organizational security standards, privacy requirements, and applicable regulatory requirements.
Apply appropriate access controls, logging, auditing, and security practices to systems containing PHI or other sensitive information.
Monitor production data workflows and proactively identify and correct failures or data-quality issues.