Leidos is seeking a Public Health Interoperability Lead to support CDC public health data modernization, interoperability, integration, and cloud-based analytics initiatives.

This position serves as the technical lead for public health data exchange, integration, interoperability, and modernization activities across CDC surveillance, research, laboratory, analytics, and operational data systems. The successful candidate will work closely with data managers, analysts, developers, architects, and program stakeholders to design, develop, implement, and support secure, scalable, and efficient data exchange solutions.

The role combines hands-on technical development with technical leadership and supports CDC modernization initiatives involving cloud-based analytics environments, enterprise data platforms, interoperability frameworks, and emerging public health technologies.

*This position requires U.S. Citizenship and is contingent upon award.

**Candidates in the Atlanta, GA area preferred. Remote work permitted with periodic travel to CDC facilities and customer meetings as required.

Primary Responsibilities

Public Health Data Integration and Interoperability

• Lead interoperability and data integration activities across CDC public health systems, platforms, and partner organizations.

• Design, develop, implement, and maintain data exchange workflows, APIs, integration services, and data pipelines.

• Support secure and efficient movement of data between surveillance, research, laboratory, operational, and reporting systems.

• Develop and maintain interface specifications, data mappings, integration documentation, and technical procedures.

• Support onboarding and integration of new data sources, systems, and external partners.

Public Health Data Modernization

• Support CDC data modernization initiatives and enterprise integration efforts.

• Lead migration of legacy SAS-based analytics, reporting, and data management workflows to modern cloud-based platforms.

• Support modernization efforts involving 1CDP, EDAV, Databricks, Azure, Python, R, and related technologies.

• Support implementation of scalable data lake, lakehouse, and modern analytics architectures.

• Collaborate with Data Governance, Data Quality, and Operations Leads to ensure interoperability solutions align with program standards and requirements.

Solution Development and Engineering

• Design, develop, test, and maintain integration and interoperability solutions.

• Develop and optimize ETL/ELT processes, APIs, data transformation workflows, and cloud-native data processing solutions.

• Develop reusable code, technical documentation, and integration patterns.

• Troubleshoot and resolve data integration, interoperability, performance, and data exchange issues.

• Participate in code reviews, testing activities, and deployment processes.

• Mentor junior developers and technical staff.

Technical Analysis and Coordination

• Analyze business and technical requirements related to interoperability, integration, and data exchange.

• Identify technical risks, integration challenges, and opportunities for modernization and process improvement.

• Coordinate testing, validation, troubleshooting, and resolution of interoperability issues.

• Participate in architecture reviews, modernization planning activities, and technical working groups.

Stakeholder Engagement and Technical Assistance

• Coordinate with CDC stakeholders, technical teams, data providers, and external partners.

• Support technical assistance activities related to interoperability and data exchange.

• Develop technical documentation, user guidance, and operational procedures.

• Present technical recommendations, architecture approaches, and modernization strategies to leadership and stakeholders.

Basic Qualifications

• Bachelor's degree in Computer Science, Data Science, Health Informatics, Public Health Informatics, Information Systems, Engineering, or related discipline.

• 8+ years of experience supporting public health data integration, interoperability, data engineering, analytics, or modernization initiatives.

• Hands-on experience developing data pipelines, ETL/ELT processes, APIs, and integration workflows.

• Strong SQL skills and experience working with relational and analytical data platforms.

• Hands-on experience with Python, PySpark, R, SAS, or comparable analytics and data engineering languages.