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Rhombus Power, Inc.

Data Engineer (Secret/Top Secret), Washington D.C.

Washington, District of Columbia, United StatesmidAdded today

About this role

Rhombus Power seeks a Data Engineer to design and implement data infrastructure for national security applications. You'll develop ETL pipelines, architect data platforms, and collaborate with analytics and ML teams to deliver mission-critical intelligence solutions.

What you'll do

  • Develop code in Python, SQL, and other languages to automate data ingestion and improve data management processes
  • Architect data repositories, design data platforms, and build ETL pipelines for ingestion, transformation, and aggregation
  • Review existing architecture and data strategy to enhance governance, quality, and metadata management
  • Extract and analyze raw data from multiple sources via APIs, SQL procedures, and Python scripts
  • Collaborate cross-functionally with analysts, data scientists, and developers in agile environments
  • Travel to client sites to discuss data pipelines, solution design, and maintain strong client relationships

What they're looking for

  • Python, Pandas, NumPy
  • SQL and relational databases (MySQL, PostgreSQL, Oracle, SQL Server)
  • NoSQL databases (MongoDB, DynamoDB, HBase)
  • ETL pipeline design and implementation
  • AWS or cloud technologies
  • Data modeling and query optimization
  • Machine learning frameworks (TensorFlow, Scikit-Learn)
  • Software development lifecycle practices
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Rhombus Power, Inc.

Rhombus Power, Inc. builds data infrastructure and software solutions for national security projects, requiring security clearances. The company is hiring DevOps engineers and data engineers to deploy systems, develop data pipelines, and support mission-critical infrastructure.

Website
rhombuspower.com
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Likely interview questions

  • Describe your experience designing and implementing ETL pipelines—what challenges have you encountered and how did you resolve them?
  • Walk us through how you've optimized database queries and data models for performance in a production environment.
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