Site Reliability Data Engineer

About this role

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. •Participate in Agile planning, backlog refinement, technical reviews, demonstrations, incident response, and after-hours support activities when required to sustain mission-critical services.

You'll Bring These Qualifications: •Requires B.S Degree and 4–8 years of prior relevant experience or Masters with 2–6 years of prior relevant experience in data engineering, computer science, information systems, software engineering, mathematics, or a related technical discipline, with at least four years of relevant experience; additional directly related experience may be considered in place of a degree. •Must be a U.S. citizen and possess an active DoD Secret Security Clearance. •Must possess and maintain an IAT Level II certification that satisfies applicable DoD cybersecurity workforce requirements. •Must be located in (or able to work onsite at Navy Base as required) either San Diego, California; the Hampton Roads, Virginia area or Jacksonville, FL •At least three years of hands-on experience developing, operating, or supporting production data pipelines, data integrations, data warehouses, data lakes, or comparable enterprise data solutions. •Demonstrated proficiency with SQL and at least one general-purpose scripting or programming language, such as Python, for data extraction, transformation, validation, automation, and troubleshooting. •Experience with ETL/ELT concepts, relational data structures, data modeling, schema design, source-to-target mapping, data quality, metadata, and lifecycle management. •Experience integrating data from multiple source types, including relational databases, APIs, flat files, application data, system logs, or message-based interfaces. •Working knowledge of cloud and on-premises infrastructure concepts, authentication and authorization, network connectivity, encryption, secure file transfer, and service accounts as they relate to data engineering. •Experience diagnosing production data issues, analyzing logs and metrics, resolving failed jobs or performance probl

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