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Accenture Federal Services

Data Engineer

Washington, DCFrom $176.2kmidAdded today

About this role

Accenture Federal Services seeks a Data Engineer to design and maintain scalable data pipelines using Databricks and Spark, while transforming diverse data sources into unified datasets that support analytics and federal government missions.

What you'll do

  • Design and build scalable end-to-end data pipelines using Databricks and Spark
  • Develop efficient data processing and transformation workflows for analytics and reporting
  • Integrate APIs, databases, and cloud storage into unified datasets
  • Collaborate with data science, analytics, and business teams on data solutions
  • Maintain and optimize data pipeline performance and reliability
  • Support federal government clients across defense, national security, and civilian sectors

What they're looking for

  • Databricks
  • Apache Spark
  • Data pipeline design and architecture
  • ETL/ELT development
  • Cloud storage and databases
  • API integration
  • SQL
  • Python or Scala

Benefits

  • Glassdoor Top 100 Best Place to Work recognition
  • Professional development through certifications and industry training
  • Collaborative and inclusive work environment
  • Hands-on learning opportunities
  • Work on meaningful federal government missions
  • Growth and career advancement potential
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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.

View all jobs at Accenture Federal Services

Likely interview questions

  • Describe your experience designing and building data pipelines with Databricks and Spark, including a specific project where you optimized pipeline performance.
  • How do you approach integrating data from multiple disparate sources (APIs, databases, cloud storage) into a unified dataset?