Accenture Federal Services
Palantir Data Engineer
About this role
Accenture Federal Services seeks an experienced Palantir Data Engineer to build and maintain data transformation pipelines for federal government clients in a small team environment. You'll develop ETL solutions using Python and TypeScript, integrate with external systems, and collaborate on data engineering challenges.
What you'll do
- Design and implement data transformation pipelines in Palantir's environment
- Develop ETL tools and conduct data ingestion for local and enterprise-level storage
- Integrate with external systems via API and parse responses into usable data objects
- Create code and documentation for data extraction, formatting, and transformations
- Conduct code reviews and participate in solution brainstorming with the team
- Perform customer triage and translate requirements into specific implementation plans
What they're looking for
- Python
- TypeScript
- ETL development and maintenance
- SQL and Apache Spark
- Cloud and on-premises data storage solutions
- Data engineering tools (Elasticsearch, NiFi)
- Palantir Foundry, Pipeline Builder, or Workshop (preferred)
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Professional development through certifications and industry training
- Collaborative and inclusive work environment
- Competitive compensation based on location and experience
- Wide variety of benefits package
- Hands-on experience and growth opportunities
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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 and maintaining ETL pipelines in Python. What tools or frameworks have you used, and what was your approach to handling data quality issues?
- Describe a time you worked with both structured and unstructured data. How did you determine the best transformation approach to meet business or mission objectives?