Data Engineer

• 3-5+ years of professional experience in data engineering, database development, software engineering, BI engineering, or a related technical discipline. \n
• Demonstrated experience designing and developing ETL/ELT processes and data pipelines. \n
• Strong experience with relational databases and SQL. \n
• Experience integrating data from multiple heterogeneous sources. \n
• Experience developing analytical data models, data warehouses, or similar repositories. \n
• Experience troubleshooting data-quality and data-integration issues. \n, * Strong analytical and problem-solving skills. \n
• Ability to understand complex relationships among applications, databases, interfaces, and business processes. \n
• Ability to translate business information requirements into technical data solutions. \n
• Understanding of transactional versus analytical data architectures. \n
• Understanding of data lineage, governance, quality, security, and lifecycle management. \n
• Ability to communicate technical concepts to both technical and nontechnical audiences. \n
• Ability to distinguish between a data problem, application problem, business-process problem, and analytical problem. \n
• Strong documentation and configuration-management discipline. \n

n \n

• \n \n

• At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers’ success. We empower our teams, contribute to our communities, and operate sustainably. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. \n \n

\n The Homeland Sector at Leidos currently has an opening for a Data Engineer with a strong background in data analytics to support an exciting DHS software development program. The Data Engineer is responsible for designing, developing, implementing, and maintaining the data architecture, pipelines, integrations, and data structures required to transform raw operational data into reliable, accessible, and analysis-ready information. The Data Engineer works across application, database, analytics, and business teams to ensure that data is accurately collected, integrated, transformed, governed, secured, and made available for reporting, business intelligence (BI), advanced analytics, and decision support. \n \n \nExpected Duties and Responsibilities \n \n \nData Architecture and Engineering \n \n \n

• Design logical and physical data models appropriate for analytical workloads. \n
• Develop data structures that integrate information from multiple applications, databases, files, APIs, logs, and external data sources. \n
• Evaluate existing data architectures and recommend improvements to reliability, scalability, performance, maintainability, and accessibility. \n
• Support the design and implementation of data warehouses, data marts, data lakes, lakehouses, and other analytical data repositories as appropriate. \n

\n \nData Integration and Pipelines \n \n \n

• Design, develop, test, deploy, and maintain ETL/ELT processes and automated data pipelines. \n
• Extract and integrate data from relational databases, applications, APIs, flat files, logs, and other structured and semi-structured sources. \n
• Develop repeatable processes for transforming raw transactional data into standardized, analysis-ready datasets. \n
• Implement scheduling, orchestration, monitoring, error handling, and recovery capabilities for data pipelines. \n
• Develop interfaces and data feeds supporting downstream reporting, dashboards, analytics platforms, and external systems. \n

\n \nData Transformation and Quality \n \n \n

• Clean, normalize, deduplicate, validate, and transform data. \n
• Investigate data discrepancies and work with application developers, database administrators, analysts, and business users to determine root causes. \n
• Implement controls that prevent data-quality problems from propagating into downstream reports and analytical products. \n

\n \nBusiness Intelligence and Analytics Support \n \n \n

• Develop curated datasets and data models optimized for BI, reporting, visualization, and analytical workloads. \n
• Work with BI analysts and business stakeholders to translate business questions into data requirements. \n
• Support development of common definitions and calculation methodologies for enterprise metrics and key performance indicators (KPIs). \n
• Support ad hoc analytical requests when new data sources or transformations are required. \n
• Assist analysts in understanding data lineage, relationships, limitations, and appropriate use. \n

\n \nData Governance and Documentation \n \n \n

• Document data sources, transformations, interfaces, business rules, dependencies, lineage, and data definitions. \n
• Maintain technical documentation for data pipelines, models, interfaces, and analytical repositories. \n
• Support development and maintenance of data dictionaries and metadata repositories. \n

\n \nDatabase and Performance Engineering \n \n \n

• Develop and optimize complex SQL queries, stored procedures, views, and data transformations. \n
• Analyze and improve performance of analytical queries and data-processing workloads. \n
• Work with Database Administrators (DBAs) to optimize database structures supporting analytical requirements. \n
• Recommend indexing, partitioning, aggregation, caching, or other techniques where appropriate. \n
• Ensure analytical workloads do not adversely affect production transactional systems. \n

\n \nSecurity and Compliance \n \n \n

• Implement data-engineering solutions consistent with organizational cybersecurity and privacy requirements. \n
• Apply appropriate access controls to sensitive data. \n
• Support data classification, encryption, auditing, logging, retention, and protection requirements. \n
• Work with cybersecurity personnel to ensure data movement and storage comply with applicable security requirements. \n
• Support protection of sensitive information, including PII and other controlled or restricted data. \n

\n \nSoftware Engineering and DevSecOps \n \n \n

• Develop maintainable data-engineering code using established software development practices. \n
• Maintain data pipeline code and configuration under version control. \n
• Participate in peer reviews, testing, configuration management, and change-control processes. \n
• Develop automated tests for data transformations and pipelines. \n
• Support CI/CD and automated deployment practices for data-engineering components. \n
• Troubleshoot production data-processing failures and perform root-cause analysis. \n, Beware of fake employment opportunities using Leidos’ name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the Leidos.com automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at LeidosCareersFraud@leidos.com. \n \n If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. \n \n \nCommitment to Non-Discrimination \n \n All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.”, “hiringOrganization”: {“@type”: “Organization”, “name”: “Leidos”, “logo”: “https://jobs.military.com/attachments/employer/0/962/152/273.svg”}, “jobLocation”: {“address”: {“addressCountry”: “United States”, “streetAddress”: “Not specified”, “@type”: “PostalAddress”, “postalCode”: “20170”, “addressLocality”: “Remote”, “addressRegion”: “Not specified”}, “@type”: “Place”}, “industry”: “Information”, “identifier”: {“@type”: “PropertyValue”, “name”: “Leidos”, “value”: “R-00193829”}, “baseSalary”: {“@type”: “MonetaryAmount”, “currency”: “USD”, “value”: {“minValue”: “131300”, “@type”: “QuantitativeValue”, “maxValue”: “237350”, “value”: “131300”, “unitText”

If you’re looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We’re not hiring followers. We’re recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We’re already at step 30 - and moving faster than anyone else dares.