CGS Federal
Data Engineer
About this role
CGS seeks a mid-level Data Engineer to design and maintain data pipelines for a federal government analytics platform. You'll collaborate with cross-functional teams to build scalable ETL solutions, ensure data quality and performance, and apply agile and CI/CD best practices while supporting mission-critical intelligence operations.
What you'll do
- Develop and optimize data pipelines to extract, process, and provision data from multiple structured and unstructured sources
- Build and maintain ETL processes with comprehensive testing and validation
- Write reliable code for data extraction and processing while monitoring performance
- Support continuous automation initiatives and CI/CD pipeline implementation
- Collaborate with program managers and engineers to translate complex requirements into technical solutions
- Contribute to cross-functional teams including designers, product managers, and other specialists
What they're looking for
- Python, SQL, R, or SAS for data manipulation
- ETL and data pipeline development
- Big data analysis and storage technologies
- Agile/Scrum methodologies and lean-agile engineering practices
- CI/CD pipelines and version control (Git)
- Relational databases (PostgreSQL)
- API design and consumption
- Linux shell scripting
Benefits
- Health, Dental, and Vision coverage
- Life Insurance
- 401(k) retirement plan
- Flexible Spending Accounts (Health, Dependent Care, Commuter)
- Paid Time Off and Federal/State Holiday observance
- Professional growth and learning opportunities
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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. What sources did you work with, and how did you handle data quality and validation?
- Describe your experience developing ETL processes. What tools or languages did you use, and how did you optimize performance for large datasets?