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 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 activities. Produce technical documentation, migration plans, data mapping specifications, and operational procedures. 4. Infrastructure, Automation & Security Develop and support containerized data services using Podman and container orchestration tools. Support applications and databases running on Red Hat Enterprise Linux and Windows Server. Automate database deployment, configuration, monitoring, and maintenance activities. Implement secure data access controls, encryption, audit logging, and data handling practices. Support deployment of software and data services in air-gapped and classified environments. Collaborate with cybersecurity and infrastructure teams to ensure compliance with applicable federal security requirements. Participate in vulnerability remediation, software evaluation, change management, and accreditation support activities. Implement monitoring and operational logging solutions using tools such as Splunk. Required Qualifications Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related technical field, or equivalent practical experience. 7+ years of experience in data engineering, database development, data integration, or related software engineering disciplines. Strong proficiency in Python for data processing, scripting, automation, and service integration. Advanced SQL skills, including query optimization, database schema design, and performance tuning. Experience administering, developing against, or optimizing PostgreSQL or comparable enterprise relational database systems. Demonstrated experience designing and implementing ETL/ELT pipelines. Experience with enterprise data migrations, including data mapping, transformation, validation, and reconciliation. Familiarity with REST APIs, JSON, structured and semi-structured data formats