Senior Data Engineer

Leidos•Tampa, FL

1d•Onsite

About The Position

Leidos is seeking highly qualified Senior Data Engineers to support a multidisciplinary team dedicated to delivering innovative planning, operational, technical, analytical, and mission support capabilities. The successful candidate will collaborate with government, military, and contractor personnel to develop solutions that enable informed decision-making, operational excellence, and mission success. This position is part of a future contract opportunity pending award announcement and requires an active TS/SCI security clearance.

Requirements

Minimum of 10 years of directly related experience in data engineering, data integration, software engineering, enterprise data management, or related technical disciplines.
Bachelor's degree in Computer Science, Data Engineering, Data Science, Information Systems, Software Engineering, Computer Engineering, or a related technical discipline. Additional experience may be considered in lieu of degree.
Demonstrated experience designing and implementing enterprise-scale data pipelines and data integration solutions.
Experience integrating data from multiple heterogeneous sources into analytical or operational environments.
Experience supporting production data environments requiring high levels of reliability, security, and availability.
Extensive experience developing ETL/ELT pipelines and enterprise data integration solutions.
Proficiency with Python, SQL, and one or more additional programming or scripting languages used for data engineering.
Experience with modern data platforms and technologies such as Databricks, Microsoft Fabric, Azure Data Factory, Snowflake, Apache Spark, Kafka, or equivalent platforms.
Experience designing and supporting data lakes, data warehouses, lake houses, and enterprise analytical environments.
Strong knowledge of relational, NoSQL, document, graph, and distributed data storage concepts.
Experience integrating data through REST APIs, web services, messaging systems, and other enterprise interfaces.
Understanding of data modeling, metadata management, data lineage, data quality, and enterprise data governance.
Experience with cloud environments such as Microsoft Azure, AWS, or equivalent enterprise cloud platforms.
Familiarity with containerization, CI/CD, Infrastructure as Code, source control, and DevSecOps practices.
Strong analytical, troubleshooting, and problem-solving skills.
Ability to communicate complex technical concepts to both technical and non-technical stakeholders.
Active TS/SCI security clearance required.

Nice To Haves

Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, or a related technical discipline.
Experience supporting Department of Defense, Intelligence Community, or other national security organizations.
Experience with Palantir Foundry, Advana, Databricks, Microsoft Fabric, or comparable enterprise data environments.
Experience developing real-time or near-real-time streaming data pipelines.
Experience supporting data environments used for AI/ML, advanced analytics, operational assessments, or mission decision support.
Experience with Kubernetes, Docker, Terraform, Git, GitLab/GitHub, or comparable DevSecOps technologies.
Cloud or data engineering certifications from Microsoft, AWS, Databricks, Snowflake, or comparable providers.

Responsibilities

Design, develop, implement, and maintain scalable data pipelines and data integration solutions supporting mission operations, analytics, reporting, and decision-making.
Integrate structured, semi-structured, and unstructured data from multiple operational, intelligence, enterprise, and external sources into secure and accessible data environments.
Develop and maintain Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows that automate data ingestion, transformation, validation, enrichment, and delivery.
Design and implement data pipelines supporting data warehouses, data lakes, lake houses, analytical platforms, dashboards, artificial intelligence/machine learning (AI/ML), and other mission applications.
Develop automated processes for data cleansing, normalization, transformation, aggregation, and quality validation to ensure reliable and consistent data products.
Collaborate with Data Architects to implement enterprise data models, architecture standards, metadata requirements, interoperability standards, and data governance practices.
Partner with Data Scientists, Data Analysts, Operations Research/Systems Analysts (ORSAs), software engineers, and mission personnel to understand data requirements and deliver fit-for-purpose data products.
Design and optimize data workflows supporting batch, near-real-time, and streaming data processing requirements.
Develop and maintain APIs, data services, and integration mechanisms that enable secure data exchange between enterprise applications and analytical environments.
Implement data quality monitoring, lineage, metadata management, logging, and observability capabilities that improve trust and transparency across enterprise data environments.
Optimize data pipelines and processing workflows for performance, scalability, reliability, availability, and cost efficiency.
Support cloud and hybrid-cloud data modernization initiatives, including migration of legacy data workflows and implementation of modern data engineering technologies.
Apply automation, Infrastructure as Code (IaC), CI/CD, and DevSecOps practices to improve repeatability, security, and reliability of data engineering solutions.
Troubleshoot complex data integration, transformation, pipeline, and performance issues and implement corrective actions.
Develop technical documentation, data flow diagrams, interface documentation, operating procedures, and implementation guidance supporting data engineering solutions.
Mentor junior technical personnel and provide guidance on data engineering methodologies, data integration, pipeline development, and engineering best practices.