The posting
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.
- Experience with Linux administration, shell scripting, and deployment automation.
- Understanding of data modeling, relational databases, data integrity, and data lifecycle management.
- Experience with Git, automated testing, and modern software development practices.
- Ability to design, document, and troubleshoot complex data workflows.
- Strong communication skills and ability to collaborate across multidisciplinary engineering teams.
Preferred Qualifications
- Experience supporting AI/ML platforms, LLM applications, or Retrieval-Augmented Generation solutions.
- Familiarity with vector databases, PostgreSQL pgvector, embeddings, and semantic search.
- Experience with document ingestion and content extraction technologies such as Docling, Apache Tika, or Tesseract.
- Experience with container technologies such as Podman or Docker.
- Familiarity with Redis and PgBouncer.
- Experience with enterprise data fabric, data mesh, or data virtualization architectures.
- Experience with Apache Airflow, Apache NiFi, or similar data pipeline orchestration frameworks.
- Experience with Apache Kafka or other event-driven data integration technologies.
- Familiarity with data catalogs, metadata management, data lineage, and data governance frameworks.
- Experience with cloud-based data platforms or hybrid data architectures, including AWS or Azure.
- Experience modernizing legacy databases and enterprise data platforms.
- Familiarity with secure, disconnected, air-gapped, or classified IT environments.
- Knowledge of federal cybersecurity standards, RMF processes, and Authority to Operate (ATO) requirements.
- Experience with Splunk, automated testing, and CI/CD practices.
The likely salary range for this position is $153,000 - $207,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
Scheduled Weekly Hours:
40
Travel Required:
Less than 10%
Telecommuting Options:
Onsite
Work Location:
USA VA Sterling
Additional Work Locations:
Total Rewards at GDIT:
Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.
Our Identity Verification Process:
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
About Our Work:
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at
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Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
