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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer | VA Claims Modernization - **Company:** LMI - **Location:** Washington, DC, United States (Remote available) - **Experience:** Expert - **Salary:** $120,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Optical Character Recognition (OCR), Audit Trail, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Cloud Database, Code Review, Information Systems, Databases, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Security, Data Systems, Data Warehousing, Relational Databases, Database Queries, Distributed Data Store, Information Extraction, Interoperability, JSON, Python (Programming Language), PostgreSQL, Metadata, Microsoft Software, Microsoft SQL Server, Natural Language Processing, Operational Databases, Power BI, Software Engineering, SQL Databases, Unstructured Data, Enterprise Data Management, Data Logging, Data Processing, Data Storage Technologies, Cloud Platform System, Azure Data Factory, Snowflake, Database Optimization, Apache Spark, Database Performance, Change Data Capture, Git, Powerquery, Microsoft Fabric, Data Lakes, Pyspark, Infrastructure Automation Frameworks, Information Technology, Data Lineage, AWS Glue, Enterprise Integration, Data Management, Data Lakehouse, Restful APIs, Software Version Control, Data Pipelines, Devsecops, Databricks - **Published:** October 23, 2026 - **Apply:** https://diversityjobs.com/career/18740122/Data-Engineer-Va-Claims-Modernization-D-C-Washington ## About the Role * 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 Spark, PySpark, or comparable distributed data-processing technologies to develop and optimize data workflows. * Hands-on experience with cloud data services in AWS, Azure, or comparable cloud environments, including data storage, processing, orchestration, and integration. * Strong understanding of relational databases, data modeling, data warehousing, and database optimization, including experience with 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., * VA / Veterans Benefits Experience: Experience supporting the Department of Veterans Affairs, Veterans Benefits Administration, disability compensation claims, claims adjudication workflows, evidence management, or other federal benefits-processing systems. * VA Systems & APIs: 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. * Modern Cloud Data Platforms: Experience with technologies such as AWS Glue, S3, Azure Data Factory, Microsoft Fabric, Databricks, Delta Lake, Snowflake, or comparable enterprise data platforms. * Data Orchestration & Advanced Engineering: Experience with Apache Airflow, event-driven data integration, change data capture, data lakehouse architectures, data lineage tooling, or modern data transformation frameworks. * Analytics & Microsoft Technologies: Familiarity with Power BI, Power Query, Microsoft Power Platform, or developing data products that support enterprise reporting and workflow applications. * AI/ML & Document Processing: 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. * Legacy Modernization: Experience modernizing legacy databases, data pipelines, or analytical processes into automated, scalable, cloud-based solutions. * Federal Delivery: Experience working in Agile or DevSecOps environments supporting federal government customers, particularly within regulated environments requiring strong data governance, security, and auditability. * Relevant cloud, data engineering, or platform certifications are a plus. ## Description 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.