Accenture Federal Services
Azure Data Engineer in St. Louis, MO
About this role
Accenture Federal Services seeks an Azure Data Engineer to design and maintain scalable data pipelines for federal government clients. You'll integrate data from multiple sources, optimize large-scale data transformations, and own end-to-end data systems while troubleshooting complex issues.
What you'll do
- Build and maintain ETL pipelines using Azure Data Factory, Event Hubs, and related services
- Develop data processing systems integrating internal and external data sources
- Write complex SQL queries and code to transform raw data into accessible models
- Clean, prepare, and optimize data at scale for analytics and reporting
- Perform root cause analysis and resolve data pipeline failures
- Create reusable components and implement data management projects
What they're looking for
- Azure cloud native technologies (3+ years)
- ETL pipeline development with Azure Data Factory
- Databricks or Apache Spark
- Python or Java programming
- SQL
- Data orchestration tools (Airflow or Oozie)
- Streaming and batch processing solutions
- DevOps, CI/CD, and SDLC practices
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Professional certifications and industry training
- Hands-on learning and growth opportunities
- Collaborative and inclusive work environment
- Comprehensive benefits package
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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 building ETL pipelines in Azure Data Factory. What was the most complex pipeline you've designed, and how did you optimize it for performance?
- Describe your hands-on experience with Databricks or Spark. How have you used these tools to process large-scale data, and what performance challenges did you encounter?