CGS Federal
Data Engineer
About this role
CGS is hiring a mid-level Data Engineer in Baltimore to design and maintain data pipelines supporting federal customers' analytics and business intelligence needs. The role combines data infrastructure development with agile team collaboration, requiring 7+ years of IT experience managing complex datasets and ETL processes.
What you'll do
- Develop and maintain end-to-end data pipelines for extraction, storage, and distribution to consumers
- Write reliable code for data processing and extraction with focus on performance
- Design and implement ETL processes with testing and validation
- Support continuous automation for data ingestion from multiple sources
- Collaborate across cross-functional agile teams to translate requirements into technical solutions
- Advocate for CI/CD practices, API-first design, and automated testing standards
What they're looking for
- Python, R, SQL, or SAS for data manipulation
- ETL development and data pipeline architecture
- Big data storage and analysis technologies
- PostgreSQL or other relational databases
- Git and version control systems
- Agile/Scrum methodologies and CI/CD pipelines
- API design and consumption
- Tableau or Power BI visualization tools (preferred)
Benefits
- Health, dental, and vision insurance
- Life insurance
- 401(k) retirement plan
- Flexible spending accounts (health, dependent care, commuter)
- Paid time off and federal holiday observance
- Professional development 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 your experience designing and building data pipelines. What sources have you integrated, and how did you handle both structured and unstructured data?
- Describe your proficiency with Python, SQL, and R. Which have you used most extensively, and can you give a specific example of a complex data manipulation task you performed?