Senior Data Engineer - Secret Clearance Required

Type of

Requisition :
Regular Clearance Level Must Currently Possess:
Secret Clearance Level Must Be Able to Obtain:
Secret Public Trust/Other

Required:

None Job Family:
Data Science and Data Engineering

Job Qualifications:

Skills:

Artificial Intelligence (AI), Data Solutions, Technical Solutions

Certifications:

None

Experience:

10 + years of related experience US Citizenship

Required:

Yes

Job Description:

Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As a Data Engineer Sr Principal at GDIT, you’ll power innovation to drive mission impact and grow your expertise to power your career forward. The ideal candidate will have strong experience with relational databases, data pipelines, ETL/ELT processes, Python development, and enterprise data integration. The candidate should also be comfortable working with containerized applications, Linux environments, and emerging AI technologies, including vector databases and Retrieval-Augmented Generation (RAG).

This individual will collaborate with software engineers, AI/ML engineers, cybersecurity professionals, database administrators, and enterprise architects to deliver scalable, secure, and reliable data solutions within highly controlled government environments. SECRET CLEARANCE REQUIRED

• Chatbot – AI Platform Data Engineering Design, develop, and maintain the data infrastructure supporting our chatbot and related AI-powered applications. Manage and optimize PostgreSQL databases, including pgvector extensions for vector similarity search. Develop and maintain data ingestion pipelines for structured, semi-structured, and unstructured data. Integrate document processing technologies such as Docling and Tesseract into automated data pipelines. Support document metadata extraction, transformation, validation, indexing, and storage.
  
  Collaborate with AI/ML engineers to optimize data retrieval, embedding storage, and RAG workflows. Develop Python-based services, scripts, and APIs to facilitate data movement between systems. Support the migration of database services and supporting components into Podman containers. Improve database reliability, performance, observability, backup procedures, and recoverability. Troubleshoot data integration issues across application services, databases, and AI inference systems.
• Enterprise Data Integration & Data Fabric Assist in evaluating and designing a potential enterprise data fabric architecture. Develop solutions for integrating data across disparate applications, databases, and enterprise systems. Design reusable data pipelines and integration patterns that support multiple applications and business domains. Support the development of enterprise data catalogs, metadata management, data lineage, and data discovery capabilities. Evaluate approaches for data virtualization, data federation, and distributed data access.
  
  Define and implement data quality, consistency, validation, and governance controls. Collaborate with enterprise architects and stakeholders to identify integration requirements and technical solutions. Help establish common data models, integration standards, and reusable data services. Evaluate open-source and commercially available data integration technologies for use in secure government environments.
• Data Migration & Modernization Plan and execute data migrations between legacy systems, modern databases, and enterprise applications. Assess source systems, database schemas, data quality, dependencies, and migration requirements. Develop ETL/ELT pipelines using Python, SQL, and appropriate data integration frameworks. Perform data extraction, transformation, cleansing, mapping, reconciliation, and validation. Design…