Data Engineer | VA Claims Modernization

LMI•Remote,

2d•$120,000 - $180,000•Remote

About The Position

LMI is seeking a talented, mission-driven Data Engineer to support the U.S. Department of Veterans Affairs, focusing on modernization of Veterans Benefits Administration claims processing. The role helps transform the collection, integration, processing, and delivery of claims information to support faster, more accurate, and more efficient services for Veterans and their families. In this hands-on engineering role, the Data Engineer designs, builds, and optimizes scalable data pipelines and modern data solutions for VA disability compensation claims, evidence management, workflow automation, and operational analytics. The work involves complex data spanning claims submissions, military service records, medical evidence, examination information, and claims processing activities, converting disparate information into secure, reliable, actionable data products. The position supports data integration and claims-processing modernization; cloud data engineering and architecture; data quality, governance, and workflow orchestration; analytics and AI enablement; and secure, compliant production operations. The engineer collaborates with VA program stakeholders, claims-processing subject matter experts, data architects, application developers, and analytics teams to translate operational needs into scalable solutions.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related technical discipline, or equivalent relevant professional experience.
At least 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 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 transformations.
Experience with Apache Spark, PySpark, or comparable distributed data-processing technologies.
Hands-on experience with cloud data services in AWS, Azure, or comparable environments, including storage, processing, orchestration, and integration.
Strong understanding of relational databases, data modeling, data warehousing, and database optimization, including SQL Server, PostgreSQL, or comparable technologies.
Experience integrating data through REST APIs, JSON, secure file exchanges, and other enterprise integration methods.
Experience implementing data-quality checks, pipeline monitoring, automated testing, version control, and CI/CD or comparable engineering practices.
Familiarity with secure data handling, access controls, and governance principles for sensitive enterprise or federal information.
Strong analytical, troubleshooting, documentation, and communication skills, with the ability to collaborate effectively with technical teams and government stakeholders.
Ability to satisfy VA personnel suitability, applicable Public Trust requirements, and federal security and privacy requirements associated with the position.

Nice To Haves

Experience supporting the Department of Veterans Affairs, Veterans Benefits Administration, disability compensation claims, claims adjudication workflows, evidence management, or other federal benefits-processing systems.
Familiarity with the Veterans Benefits Management System (VBMS), VA Lighthouse Benefits Claims or Benefits Intake APIs, electronic claims folders, or comparable federal benefits data environments.
Experience with AWS Glue, S3, Azure Data Factory, Microsoft Fabric, Databricks, Delta Lake, Snowflake, or comparable enterprise data platforms.
Experience with Apache Airflow, event-driven data integration, change data capture, data lakehouse architectures, data lineage tooling, or modern data transformation frameworks.
Familiarity with Power BI, Power Query, Microsoft Power Platform, or data products supporting enterprise reporting and workflow applications.
Experience preparing governed datasets for AI/ML use cases, document intelligence, OCR, natural language processing, or automation involving large volumes of claims documents and supporting evidence.
Experience modernizing legacy databases, data pipelines, or analytical processes into automated, scalable, cloud-based solutions.
Experience in Agile or DevSecOps environments supporting federal government customers, particularly in regulated environments requiring strong data governance, security, and auditability.
Relevant cloud, data engineering, or platform certifications.

Responsibilities

Design, develop, and maintain scalable, secure ETL/ELT pipelines using Python, SQL, PySpark, and modern data engineering frameworks.
Integrate structured, semi-structured, and unstructured information from authorized VA systems, claims applications, evidence repositories, medical records, external sources, and enterprise databases using REST APIs, JSON, and secure data exchange methods.
Engineer data solutions that streamline claims intake, evidence collection, validation, workflow routing, status tracking, and downstream processing; reduce manual reconciliation and redundant data movement; and improve access to accurate claims information.
Develop and optimize cloud data solutions using approved AWS, Azure, or comparable environments and cloud-native processing, storage, orchestration, and analytics services.
Design and maintain relational, dimensional, and analytical data models for claims operations, reporting, and enterprise data products; apply data lake, data warehouse, and lakehouse architecture patterns.
Use Apache Spark, PySpark, or other distributed processing technologies to transform large-scale datasets and optimize SQL queries, database performance, transformations, and processing workflows.
Establish automated data validation, reconciliation, monitoring, and quality controls; maintain metadata, lineage, traceability, and documented business rules.
Build repeatable batch, incremental, and event-driven workflows with orchestration tools, automated testing, scheduling, error handling, logging, and monitoring.
Develop trusted, analysis-ready datasets and reusable data products for operational dashboards, Power BI reporting, claims performance analysis, predictive analytics, and approved AI/ML-enabled administrative workflows, preserving government oversight of benefits decisions.
Implement secure data-handling practices, including encryption, role-based access controls, auditability, and data minimization, in alignment with applicable VA security, privacy, federal records-management, and FISMA requirements and protections for PII and PHI.
Apply software engineering best practices, including Git-based version control, code reviews, CI/CD, automated testing, documentation, and infrastructure as code where applicable.
Collaborate with VA stakeholders, claims-processing subject matter experts, data architects, application developers, and analytics teams to turn operational requirements into production-ready data engineering solutions.
Identify opportunities to modernize legacy integrations, improve processing performance and interoperability, and improve claims workflow efficiency, data reliability, and operational decision-making.