• Design, develop, and maintain end-to-end data pipelines using Databricks (Spark, PySpark, SQL) to ingest, transform, and curate structured and semi-structured data from authoritative DoD source systems. • Engineer and optimize data models, Delta tables, and curated datasets to support financial reporting, PPBE workflows, auditability, and mission analytics within Advana and downstream Foundry use cases. • Develop, maintain, and deploy Foundry pipelines, transforms, and data products, enabling traceability, lineage, and repeatable analytics workflows for DoD stakeholders. • Implement and manage CI/CD and version-controlled data engineering workflows using Git-based repositories and automated deployment patterns aligned with DoD security and ATO requirements. • Build and deploy cloud-native solutions in secure AWS and Azure environments, supporting IL4/IL5 architectures as applicable, and leveraging containerization where approved. • Partner with functional SMEs, financial analysts, platform engineers, and governance teams to ensure data pipelines conform to DoD financial data standards, business rules, and controls. • Implement robust data quality, reconciliation, validation, and monitoring capabilities to support financial accuracy, audit readiness, and confidence in reported metrics. • Support data integration, modernization, and migration efforts, including onboarding legacy financial systems and transitioning workloads to Databricks and Foundry within Advana. • Troubleshoot and resolve pipeline, performance, and data integrity issues in production environments, ensuring high availability and reliability in mission-critical systems. • Document data flows, engineering designs, and operational procedures to support compliance, knowledge transfer, and platform accreditation. • Stay current on emerging data engineering, analytics platform, and DevSecOps best practices relevant to DoD enterprise data environments.

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

• Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code