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.
Key Responsibilities
1. 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.
2. 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.
3. 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 repeatable migration processes that minimize operational risk and data loss.
Support migration testing, performance optimization, rollback planning, and post-migration verification.
Develop scripts and automation to streamline recurring data migration…