CGS Federal
Data Engineer
About this role
CGS seeks a mid-level Data Engineer to design and maintain data pipelines for a federal analytics platform serving government clients. You'll develop ETL processes, manage large complex datasets, and collaborate with cross-functional teams to deliver actionable insights.
What you'll do
- Design and develop data pipelines for extraction, storage, and provisioning to end users
- Build and optimize ETL processes handling structured and unstructured data from multiple sources
- Write and maintain code ensuring data processing performance and reliability
- Implement continuous automation for data ingestion workflows
- Collaborate with program management and engineers to document and implement complex requirements
- Contribute to CI/CD pipelines and maintain technical excellence through lean-agile practices
What they're looking for
- Data pipeline development and architecture
- ETL process design and implementation
- Python, SQL, R, or SAS programming
- Big data tools and technologies
- Relational databases (PostgreSQL)
- Git version control
- CI/CD pipeline knowledge
- Agile/Scrum practices
Benefits
- Health, Dental, and Vision insurance
- Life Insurance
- 401(k) retirement plan
- Flexible Spending Accounts
- Paid Time Off and Federal Holiday observance
- Professional growth and learning opportunities
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
CGS Federal
CGS Federal provides workforce development solutions and federal technology services, including training programs, job placement initiatives, and custom software development for government agencies. The company is hiring workforce development professionals in Washington, DC and SharePoint/PowerApps developers in Chicago and Atlanta to support these initiatives.
- Website
- cgsfederal.com
Likely interview questions
- Walk us through a complex data pipeline you've designed end-to-end—from ingestion through storage to provisioning for consumers. What challenges did you face?
- Describe your experience developing ETL processes. How do you ensure data quality and validation across structured and unstructured datasets in different formats?