Data Engineer

Type of Requisition:

Regular

Clearance Level Must Currently Possess:

Secret

Clearance Level Must Be Able to Obtain:

Top Secret/SCI

Public Trust/Other Required:

None

Job Family:

Data Science and Data Engineering

Job Qualifications:

Skills:

Analytics, Data Access, Data Analysis, Data Transformation, Enterprise Data Certifications: None Experience: 3 + years of related experience US Citizenship Required: Yes

Job Description:

We are GDIT. The people supporting and securing some of the most complex government, defense,

and intelligence projects across the country. We ensure today is safe and tomorrow is smarter. Our work has meaning and impact on the world around us, but also on us, and that’s important.

GDIT is your place. You make it your own by embracing autonomy, seizing opportunity, and being rusted to deliver your best every day.

We think. We act. We deliver. There is no challenge we can’t turn into opportunity. And our work depends on a Data Engineer joining our team to support customer activities.

The Data Engineer builds and sustains the data foundation of the POTFF Enterprise Data and Analytics function. The Data Engineer acquires, integrates, and prepares data from the Government-designated Human Performance System of Record (e.g., HPDMS) and other Government-approved sources; designs, implements, and maintains scalable, reproducible data pipelines and curated, analysis-ready datasets on the Government-furnished technology stack; and enforces the data quality, lineage, and governance controls that make downstream analysis defensible. The Data Engineer provides data engineering expertise for the enterprise data science team and works in direct support of the Data Scientists, Senior Data Scientists, and Data Science Advisor.

HOW A DATA ENGINEER WILL MAKE AN IMPACT:

• Design, implement, and maintain scalable extract-transform-load and extract-load-transform (ETL/ELT) pipelines on the Government-furnished technology stack (e.g., SQL Server, Databricks, and Apache Spark (PySpark)) for batch and scheduled processing and data transformation.

• Fuse disparate structured, semi-structured, and unstructured data sources from across the POTFF enterprise, including the Government-designated Human Performance System of Record (e.g., HPDMS), assessment and wearable data, and other Government-approved systems, into curated, analysis-ready datasets.

• Implement rigorous data quality controls, validation rules, lineage documentation, and performance tuning to ensure reliable, reproducible, and secure pipelines; monitor scheduled and orchestrated workloads and resolve failures.

• Develop and maintain data models, data dictionaries, metadata, and transformation logic under version control, and document data sources, assumptions, and processing steps to ensure transparency and operational continuity.

• Prepare and curate text and other unstructured data to support Natural Language Processing (NLP) and Large Language Model (LLM) use cases executed by the data science team within Government-approved tools.

• Support the integration of curated data products into Government-furnished visualization and low-code tools and the deployment of analytic products into operational workflows, including basic machine-learning operations (MLOps) practices where directed by the Government.

• Apply Government data governance, security, and privacy requirements, including DoW Impact Level 5 (IL5), HIPAA, and Privacy Act protocols, to all data handling, and implement data access controls as directed by the Government.

• Collaborate with Government personnel, clinicians, program staff, and the data science team to identify new data sources, improve the quality of data entered into Government systems, and translate analytical requirements into data requirements.

• Provide data engineering expertise and technical guidance to Data Scientists and Senior Data Scientists.

WHAT YOU’LL NEED TO SUCCEED:

EDUCATION:

• Bachelor’s degree in a quantitative or computing discipline, including but not limited to computer science, computer engineering, software engineering, information systems, data science, data engineering, mathematics, statistics, or geospatial science or geoinformatics, with documented coursework or professional application in programming, databases, and quantitative analysis. A candidate without the degree qualifies with the experience stated under Required Minimum Experience/Skills.

• Minimum of three (3) years of professional experience in data engineering, database development, or data pipeline development. Without the bachelor’s degree, a minimum of five (5) years of such experience.

• Demonstrated proficiency in SQL and in Python or a comparable language used for data engineering, demonstrated through professional work experience.

• Demonstrated experience building and operating data pipelines, including ETL/ELT design, data modeling, data quality checks, reproducible pipeline design, and version control…