Accenture Federal Services
Data Engineer
About this role
Accenture Federal Services seeks a Data Engineer to design and maintain scalable data pipelines while collaborating with cross-functional teams to deliver data solutions for federal government clients. The role focuses on building efficient data transformation workflows, integrating diverse data sources, and supporting analytics initiatives across defense, national security, and public safety organizations.
What you'll do
- Design and maintain scalable end-to-end data pipelines using Databricks and Spark
- Develop efficient data processing and transformation workflows for analytics and reporting
- Integrate diverse data sources including APIs, databases, and cloud storage
- Collaborate with data science, analytics, and business teams on data solution design
- Support federal government missions across defense, national security, and public safety sectors
What they're looking for
- Databricks
- Apache Spark
- Data pipeline architecture
- ETL/data transformation
- API integration
- Cloud storage platforms
- SQL
- Python or Scala
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Professional certifications and industry training
- Hands-on experience and learning opportunities
- Collaborative and inclusive work environment
- Career growth and development programs
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Accenture Federal Services
Accenture Federal Services builds and maintains mission-critical technology solutions for the U.S. federal government, including cloud infrastructure, enterprise systems integrations, and cyber defense tools. The company is hiring DevOps engineers, full-stack developers, SAP specialists, and test engineers to support classified and unclassified federal government projects.
- Website
- accenturefederal.com
Likely interview questions
- Walk us through your experience designing and building end-to-end data pipelines using Databricks and Spark. What challenges did you face with scalability?
- How have you integrated diverse data sources—such as APIs, databases, and cloud storage—into unified datasets? What approach did you take?