Data Engineer

General Dynamics Information Technology, Inc.

Joint Base Lewis-McChord (WA)

On-site

USD 120,000 - 170,000

Full time

3 days ago

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Benefits offered by this job

401K with company match

Comprehensive health and wellness

Internal mobility support

Professional growth opportunities

Cutting-edge technology exposure

Paid vacation and holidays

Job summary

General Dynamics Information Technology, Inc. (GDIT) seeks a Data Engineer to build and sustain data pipelines for the POTFF Enterprise Data and Analytics function. You will work with HPDMS data and other government sources to create analysis-ready datasets in a compliant environment.

You will design scalable ETL/ELT processes, ensure data quality and governance, and collaborate with data scientists and government teams to deliver analytics products and MLOps-ready workflows.

Qualifications

Bachelor’s degree in a quantitative or computing discipline with coursework in programming, databases, and quantitative analysis; a candidate without the degree qualifies with relevant experience.
Minimum of three (3) years of professional experience in data engineering, database development, or data pipeline development.
Demonstrated proficiency in SQL and in Python or a comparable language used for data engineering.
Experience building and operating data pipelines, including ETL/ELT design, data modeling, data quality checks, reproducible pipeline design, and version control (Git).
Ability to communicate clearly with analysts and non-technical stakeholders and to document technical work for continuity.

Responsibilities

Design, implement, and maintain scalable ETL/ELT pipelines on the government-furnished tech stack.
Fuse disparate data sources into curated, analysis-ready datasets.
Implement data quality controls, lineage, and governance; monitor workloads and resolve failures.
Develop and maintain data models, dictionaries, metadata, and transformation logic under version control.
Prepare and curate text and other unstructured data for NLP/LLM use cases.
Support integration of data products into government visualization and low-code tools; assist in deployment to workflows; apply MLOps concepts as directed.

Skills

SQL

Python

Data pipelines

Documentation

Communication

Education

Bachelor’s degree in a quantitative or computing discipline

Tools

Palantir Foundry

Databricks

Apache Spark (PySpark)

Power BI

Power Platform

Git

Job description

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

andintelligence projects across the country. We ensuretoday is safe and tomorrow is smarter. Our work hasmeaning and impact on the world aroundus, but also on us, andthat’simportant.

GDIT is your place. You make it your own by embracingautonomy, seizing opportunity, and being rusted todeliver your best every day.

We think. We act. We deliver. There is no challenge wecan’tturn into opportunity. And our work depends on a Data Engineer joining our team to supportcustomer 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 (Git).