Accenture Federal Services
Enterprise Data Engineer
About this role
Accenture Federal Services seeks an experienced Data Engineer to design and maintain ETL pipelines using Python, SQL, and Palantir for federal government clients. You'll build robust data integration solutions, ensure data quality, and optimize performance across diverse data sources in a security-cleared role.
What you'll do
- Write Python and SQL scripts to automate data processing and ETL workflows
- Integrate data from multiple sources including databases, APIs, and file systems into centralized platforms
- Implement data transformations, enrichment, and quality validation mechanisms
- Optimize data structures for performance and scalability
- Provide technical guidance on Palantir tools and data platform usage
- Maintain data accuracy and consistency through validation and cleansing processes
What they're looking for
- Python programming and scripting
- SQL querying and optimization
- ETL and data integration
- Palantir platform experience
- Cloud platforms (AWS, Azure, GCP)
- Big data technologies (Apache Spark, Hadoop)
- Data orchestration tools (Apache Airflow, Prefect)
- Data quality and validation
Benefits
- Collaborative work environment recognized as Glassdoor Top 100 Best Place to Work
- Professional development through certifications and industry training
- Hands-on learning and growth opportunities
- Work supporting U.S. federal government missions
- Competitive compensation based on location and experience
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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 a recent ETL pipeline you designed using Python and SQL. What challenges did you encounter and how did you optimize it for performance?
- Describe your hands-on experience with Palantir. How have you used it to transform and integrate data from multiple sources?