Data Engineer | VA Claims Modernization

Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines to modernize VA claims processing and data integration. Collaborate with cross-functional teams to build secure, reliable data products that support operational analytics and workflow automation.

Requirements

Requires a bachelor's degree in a technical field and at least 5 years of experience in data engineering or software development. Candidates must possess strong proficiency in Python, SQL, and cloud-based data processing technologies.

Skills

• Python
• SQL
• PySpark
• ETL/ELT Pipelines
• Cloud Data Engineering
• Data Modeling
• AWS
• Azure
• Data Integration
• Workflow Automation
• Data Quality
• Big Data Processing
• DevSecOps
• API Integration
• Data Governance
• Apache Spark

About this role

Overview
LMI is seeking a talented and mission-driven Data Engineer to support the U.S. Department of Veterans Affairs (VA), with a focus on modernizing Veterans Benefits Administration (VBA) claims processing. This role will help transform how critical claims information is collected, integrated, processed, and delivered, enabling faster, more accurate, and more efficient services for Veterans and their families.
In this hands-on engineering role, you will design, build, and optimize scalable data pipelines and modern data solutions that support VA disability compensation claims, evidence management, workflow automation, and operational analytics. You will work with complex datasets spanning claims submissions, military service records, medical evidence, examination information, and claims processing activities, transforming disparate information into secure, reliable, and actionable data products.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.
Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
Responsibilities
Data Pipeline Engineering: Design, develop, and maintain scalable, secure ETL/ELT pipelines using Python, SQL, PySpark, and modern data engineering frameworks to support VA claims processing, data integration, and analytics.
VA Claims Data Integration: Integrate structured, semi-structured, and unstructured data from authorized VA systems, claims applications, evidence repositories, medical records, external data sources, and enterprise databases. Develop reusable integrations using REST APIs, JSON, and secure data exchange methods.
Claims Processing Modernization: Engineer data solutions that streamline claims intake, evidence collection, validation, workflow routing, status tracking, and downstream processing. Help reduce manual reconciliation, eliminate redundant data movement, and improve access to accurate claims information.
Cloud Data Engineering: Develop and optimize solutions using approved AWS, Azure, or comparable cloud environments, leveraging cloud-native data processing, storage, orchestration, and analytics services.
Data Architecture & Modeling: Design and maintain relational, dimensional, and analytical data models supporting claims operations, reporting, and enterprise data products. Apply modern data lake, data warehouse, and lakehouse architecture patterns to improve scalability, accessibility, and performance.
Big Data Processing: Use distributed processing technologies such as Apache Spark and PySpark to transform and process large-scale datasets. Optimize SQL queries, database performance, transformations, and data-processing workflows for reliability and efficiency.
Data Quality & Governance: Establish automated data validation, reconciliation, monitoring, and quality controls to improve accuracy, completeness, consistency, and timeliness. Maintain metadata, data lineage, traceability, and documented business rules across critical claims datasets.
Workflow Automation & Orchestration: Build repeatable batch, incremental, and event-driven data workflows using modern orchestration tools. Implement automated testing, scheduling, error handling, logging, and monitoring to support dependable production operations.
Analytics & AI Enablement: Develop trusted, analysis-ready datasets and reusable data products supporting operational dashboards, Power BI reporting, claims performance analysis, predictive analytics, and approved AI/ML-enabled administrative workflows. Support responsible automation while preserving government oversight of benefits decisions.
Security & Compliance: Implement secure data-handling practices, including encryption, role-based access controls, auditability, and data minimization. Ensure solutions align with applicable VA security, privacy, federal records-management, and FISMA requirements, including appropriate protections for PII and PHI.
DevSecOps & Engineering Best Practices: Apply software engineering principles, including Git-based version control, code reviews, CI/CD pipelines, automated testing, documentation, and infrastructure as code where applicable.
Cross-Functional Collaboration: Partner with VA program stakeholders, claims processing subject matter experts, data architects, application developers, and analytics teams to translate operational requirements into scalable, production-ready data engineering solutions.
Continuous Improvement: Identify opportunities to modernize legacy integrations, improve data-processing performance, strengthen system interoperability, and deliver measurable improvements in claims workflow efficiency, data reliability, and operational decision-making.
Qualifications
Minimum Qualifications
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related technical discipline, or equivalent relevant professional experience.
5+ years of experience in data engineering, software engineering, enterprise data integration, or development of production data systems.
Strong hands-on proficiency in Python and SQL, including developing complex queries, data transformations, automation scripts, and production data pipelines.
Experience designing, developing, and maintaining scalable ETL/ELT pipelines involving multiple enterprise data sources and complex data transformation requirements.
Experience using Apache...

About LMI

Software Development · 1,001-5,000 employees · headquartered in Tysons, VA · founded 1961