Site Reliability Data Engineer

More About the Role: Leidos is seeking a Site Reliability Engineer (SRE) Data Engineer supporting the largest IT services program for the Navy. Under the Service Management, Integration, and Transport (SMIT) program, the Leidos team delivers the core backbone of the Navy-Marine Corps Intranet, including cybersecurity services, network operations, service desk, and data transport. Leidos supports the Navy in unifying its shore-based networks and data management to improve capability and service while also saving significant dollars by focusing efforts under one enterprise network. As part of the SRE organization you will develop and execute tests focused on system resilience, performance underload, and failure scenarios. You will also work in tandem with other Site Reliability Engineers (SREs) and development teams to create automated testing frameworks that simulate real-world conditions that validate system behavior under normal and stress conditions, ensuring our services are resilient and meet established service level objectives (SLOs) The SRE will support the operations and maintenance of the enterprise network. Your work will contribute to the development of robust and scalable services that operate reliably in production. The Data Engineer designs, builds, integrates, and sustains secure enterprise data solutions that enable reliable analytics, reporting, operational insight, and informed decision-making. The position develops scalable data pipelines, integrates data from diverse sources, improves data quality and availability, and supports cloud and on-premises platforms in a mission-critical environment. The Data Engineer works closely with business intelligence, cybersecurity, infrastructure, application, and program teams to translate operational requirements into governed, supportable, and reusable data products; and will contribute to modernization activities in an Agile/DevOps operating culture. What You'll Get to Do: •Design, develop, test, deploy, and maintain scalable ETL/ELT pipelines that collect, transform, validate, and deliver structured and unstructured data from enterprise systems, applications, APIs, logs, files, and databases. •Build and optimize data models, schemas, tables, views, and curated data sets that support analytics, business intelligence, operational reporting, and downstream application requirements. •Develop data-processing solutions using SQL, Python, and other approved technologies; apply reusable engineering patterns, source control, peer review, and documented release practices. •Integrate cloud and on-premises data platforms while supporting secure data movement, interoperability, availability, retention, and performance across hybrid enterprise environments. •Implement automated data-quality checks, reconciliation controls, monitoring, alerting, and exception handling to identify incomplete, inaccurate, duplicated, delayed, or failed data flows. •Troubleshoot pipeline failures, data discrepancies, performance degradation, access issues, and integration defects; perform root-cause analysis and implement corrective and preventive actions. •Partner with analysts, business intelligence developers, data owners, system administrators, and mission stakeholders to define data requirements, source-to-target mappings, transformation rules, service expectations, and acceptance criteria. •Apply data governance, security, privacy, least-privilege access, auditability, and records-retention requirements throughout the data lifecycle in coordination with cybersecurity and compliance teams. •Support platform upgrades, data migrations, modernization initiatives, capacity planning, and performance tuning while minimizing disruption to production services. •Create and maintain technical documentation, including architecture diagrams, data dictionaries, lineage documentation, interface specifications, runbooks, standard operating procedures, and troubleshooting guides.