Data Engineer

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.

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.

Expected Duties and Responsibilities

Data Architecture and Engineering

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

Data Integration and Pipelines

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

Data Transformation and Quality

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

Business Intelligence and Analytics Support

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

Data Governance and Documentation

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

Database and Performance Engineering

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

Security and Compliance

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

Software Engineering and DevSecOps

• •Develop maintainable data-engineering code using established software development practices.
• •Maintain data pipeline code and configuration under version control.
• •Participate in peer reviews, testing, configuration management, and change-control processes.
• •Develop automated tests for data transformations and pipelines.
• •Support CI/CD and automated deployment practices for data-engineering components.
• •Troubleshoot production data-processing failures and perform root-cause analysis.

Basic Qualifications

• •Must be a U.S. Citizen
• •Must be capable of receiving a Public Trust clearance level.
• •Must have verifiable accredited education.

Education Requirements

Bachelor's degree in Computer Science, Data Science, Information Systems, Software Engineering, Computer Engineering, Mathematics, Statistics, or a related technical discipline.

Experience Requirements

• •3–5+ years of professional experience in data engineering, database development, software engineering, BI engineering, or a related technical discipline.
• •Demonstrated experience designing and developing ETL/ELT processes and data pipelines.
• •Strong experience with relational databases and SQL.
• •Experience integrating data from multiple heterogeneous sources.
• •Experience developing analytical data models, data warehouses, or similar repositories.
• •Experience troubleshooting data-quality and data-integration issues.

Preferred Qualifications

Experience with technologies such as Azure, AWS, Snowflake, Databricks, Spark, Airflow, dbt, Kafka, Power BI, Tableau, or similar platforms.

Required Technical Skills

The candidate should possess strong proficiency in several of the following areas:

• •Advanced SQL and relational database concepts
• •Oracle, SQL Server, PostgreSQL, or comparable RDBMS technologies
• •Python or another data-processing/programming language
• •ETL/ELT development
• •Data modeling and dimensional modeling
• •Data warehouse architecture
• •REST APIs and system integration
• •Data validation and quality management
• •Git or comparable version-control systems
• •Automated testing and CI/CD concepts
• •Structured and semi-structured data processing

Knowledge and Competencies

The successful candidate should demonstrate:

• •Strong analytical and problem-solving skills.
• •Ability to understand complex relationships among applications, databases, interfaces, and business processes.
• •Ability to translate business information requirements into technical data solutions.
• •Understanding of transactional versus analytical data architectures.
• •Understanding of data lineage, governance, quality, security, and lifecycle management.
• •Ability to communicate technical concepts to both technical and nontechnical audiences.
• •Ability to distinguish between a data problem, application problem, business-process problem, and analytical problem.
• •Strong documentation and configuration-management discipline.

► Please be advised that during the interview, you will be required to keep your camera on, and your interviewer will be taking your picture for identification purposes if an offer letter is extended to you.

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