Lead Data Analyst

Leidos•Reston, VA

About The Position

Every day, more than 9 million Veterans rely on The Department of Veterans Affairs (VA) for their healthcare, supported by a nationwide health IT system. Leidos is supporting this transformation effort, offering an opportunity to make a direct impact on how care is delivered to those who’ve served. The Leidos Health & Services Sector is seeking a full-time Lead Data Analyst to support the Department of Veterans Affairs (VA) to help transform how it collects, manages, analyzes, and utilizes health data, to transform and improve care for Veterans. The Lead Data Analyst serves as the primary authority for data analysis and reporting across Agile integration, interoperability, and clinical informatics initiatives under the task order. This individual is responsible for designing and maintaining data pipelines, performing advanced analytics, and ensuring data integrity to support clinical and operational decision-making across VA's health informatics programs. The Lead Data Analyst plays a critical role in supporting the integration of multiple health IT systems — spanning the legacy VistA/CPRS environment and Oracle Health Cerner Millennium — by validating data migration fidelity, ensuring interoperability compliance, and translating complex data findings into actionable insights for program leadership and VA stakeholders. Within the program's SAFe Agile delivery structure, this individual supports PI Planning analytics, backlog performance analysis, and velocity metrics reporting that keep the Agile Release Train (ART) informed and on track. This role sits at the intersection of clinical data governance, health IT interoperability, and enterprise analytics — making it one of the most technically integrative positions on the program. The Lead Data Analyst is expected to operate across the VA Corporate Data Warehouse (CDW), VINCI analytic environment, and Power BI reporting platforms while maintaining strict compliance with VA data governance, HIPAA security rules, and FISMA requirements.

Requirements

Bachelor's degree in Data Science, Computer Science, Health Informatics, Statistics, or a closely related field.
Minimum 8 years of professional experience in data analytics or health informatics with demonstrated leadership responsibilities.
Minimum 5 years of direct experience working with healthcare data standards including HL7 FHIR, SNOMED CT, LOINC, and interoperability frameworks.
Demonstrated proficiency in SQL and at least one of the following analytical programming languages: Python or R.
Hands-on experience with ETL (Extract, Transform, Load) processes and data pipeline design in healthcare IT environments.
Experience with clinical systems including VA VistA/CPRS and/or Oracle Health Cerner Millennium.
Proficiency with data visualization tools such as Power BI or Tableau.
Demonstrated experience in Agile environments supporting PI Planning analytics, backlog metrics, and velocity reporting.
Familiarity with HIPAA compliance requirements for healthcare data governance environments.
U.S. citizenship required; must be eligible for VA Suitability determination and PIV credentialing.

Nice To Haves

Master's degree in Health Informatics, Data Analytics, Public Health, or a related quantitative discipline.
Certified Health Data Analyst (CHDA) credential from AHIMA.
Certified Scrum Master (CSM) or SAFe Practitioner certification demonstrating familiarity with Agile delivery frameworks.
Direct experience supporting VA data environments including VINCI, VA CDW, or VIReC data resources.
Experience with data migration validation in the context of VA's EHR Modernization (EHRM) program using Oracle Health Cerner Millennium.
Familiarity with VA ProPath project management methodology and VA OIT data governance processes.
Experience with HL7 FHIR resource validation, SMART on FHIR applications, or FHIR-based interoperability testing tools.
Experience producing analytics deliverables within a FedRAMP-authorized cloud or Microsoft Azure Government environment.

Responsibilities

Serve as the primary data analysis authority for the task order, providing expert-level oversight across all data analytics, reporting, and data pipeline activities in support of clinical informatics, interoperability, and Agile integration workstreams.
Design, build, and maintain data pipelines that support the movement, transformation, and validation of clinical data across VistA/CPRS, Oracle Health Cerner Millennium, and VA enterprise data repositories including the VA Corporate Data Warehouse (CDW) and VINCI environment.
Perform advanced analytics and data quality assessments to validate data migration fidelity during VistA-to-Cerner transition activities, identifying data integrity issues, mapping discrepancies, and recommending remediation strategies.
Develop and maintain Power BI dashboards and reporting products that provide program leadership and VA stakeholders with real-time visibility into clinical informatics performance, interoperability validation results, and Agile delivery metrics.
Support SAFe PI Planning activities by providing data-driven analytics on backlog health, sprint velocity, team capacity, and program predictability metrics for the Agile Release Train (ART).
Validate interoperability workflows and HL7 FHIR interface data integrity, ensuring that clinical data exchanged across systems is accurate, complete, and correctly mapped to applicable terminology standards including SNOMED CT, LOINC, ICD-10-CM/PCS, and RxNorm.
Develop and maintain terminology mapping crosswalks that support data standardization and semantic interoperability across VA health IT systems.
Ensure all data analytics activities comply with VA data governance policies, HIPAA Security Rule requirements, FISMA controls, VA Handbook 6500, and applicable VA Directives governing the handling of PHI and PII.
Produce data analysis deliverables in Microsoft Office-compatible formats in accordance with Section 508 accessibility requirements.
Provide mentorship and technical direction to junior data analysts on the program team, establishing data quality standards, pipeline documentation practices, and analytic methodology guidelines.