CGS Federal
Data Engineer
About this role
CGS is hiring a mid-level Data Engineer in Miami to build and maintain data pipelines and analytics solutions for federal government clients. The role focuses on designing ETL processes, ensuring data quality, and collaborating with cross-functional teams in an Agile environment to deliver actionable insights.
What you'll do
- Design and develop end-to-end data pipelines for ingestion, storage, and distribution to data consumers
- Write and optimize code for reliable data extraction, transformation, and processing
- Develop and maintain ETL processes with comprehensive testing and validation
- Implement continuous process automation and CI/CD pipelines
- Collaborate with program management and engineers to translate complex requirements into technical solutions
- Support cross-functional teams including product managers, UX researchers, and designers
What they're looking for
- Data pipeline design and ETL development
- Python, R, SQL, or SAS for data manipulation
- Big data analysis and storage technologies
- PostgreSQL and relational databases
- Git and version control systems
- Agile/Scrum methodologies and CI/CD pipelines
- API design and consumption
- Tableau or PowerBI visualization
Benefits
- Health, Dental, and Vision insurance
- Life Insurance and 401k retirement plan
- Flexible Spending Accounts (Health, Dependent Care, Commuter)
- Paid Time Off and State/Federal Holiday observance
- Professional growth and learning opportunities
- Collaborative, supportive work environment
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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 your experience designing and building data pipelines from multiple sources with both structured and unstructured data. What challenges did you face and how did you overcome them?
- Describe your experience with ETL processes—what tools have you used, and can you give an example of how you validated data quality and handled failures in a pipeline?