Senior Data Engineer - Leidos

Description

Are you ready to join Leidos all-star team? Through training, teamwork, and exposure to challenging technical work, let Leidos show how to accelerate your career path.

Leidos is seeking a mission-driven Senior Data Engineer to join our high-impact team supporting the National Digital Exploitation and Open Source Center (NDOC) DOMEX Technology Platform (DTP). In this role, you will help improve data intelligence pipelines serving both Open Source Intelligence (OSINT) operations and document media exploitation for the Department of War (DoW) and the broader Intelligence Community (IC). You’ll play a critical role in developing a future state AI-managed DOMEX Data Discovery Platform (D3P) and DIA-NDOC’s mission.

As a key technical leader, you will collaborate with talented engineers, product owners, and government stakeholders to design, develop, and deploy innovative software solutions that make a real difference to national security. You’ll have the opportunity to shape the architecture, drive technical direction, and mentor others, all while working in a collaborative, agile, and growth-oriented environment.

You will work closely with the team on the following key tasks

As a Data Engineer, you will design, develop, and optimize scalable data management solutions supporting advanced analytics and AI/ML workloads. Working closely with data scientists, software engineers, and infrastructure teams, you will build and maintain Elasticsearch and graph-based data platforms that enable efficient storage, discovery, and analysis of complex hierarchical data.

• Design and optimize Elasticsearch indices to efficiently manage large-scale hierarchical classification structures, ensuring high-performance search, aggregation, and update operations.
• Design, implement, and maintain a graph data store supporting hierarchical graph structures optimized for large, dense batch updates and high-throughput analytics.
• Develop scalable ingestion pipelines that support scalable graph analytics.
• Design data schemas, indexing strategies, and partitioning approaches to maximize query performance, scalability, and storage efficiency.
• Collaborate with infrastructure and platform engineering teams to deploy, operate, and optimize Elasticsearch and graph database services in a Kubernetes environment using cloud-native technologies.
• Partner with Data Scientists and AI/ML engineers to ingest, curate, manage, analyze, and securely expunge datasets throughout the data lifecycle.
• Develop automated data loading, transformation, validation, and quality assurance pipelines supporting production analytics workflows.
• Optimize graph and search infrastructure for high availability, resilience, and performance under large-scale analytical workloads.