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Captivation Software

Software Engineer 2 - Spark/MapReduce/SQL/NoSQL/Pandas/Numpy/SciPy

Annapolis Junction, MD$130k–$270kmidAdded today

About this role

Captivation Software seeks a mid-level cloud-based analytics developer to build high-performance distributed data processing solutions. You'll work with big data technologies to handle massive data volumes while supporting national security missions.

What you'll do

  • Develop and maintain cloud-based analytics solutions processing large-scale data
  • Design and optimize distributed computing pipelines using Spark and MapReduce
  • Build and query SQL/NoSQL databases for analytical workloads
  • Implement data analysis and transformation using Python scientific libraries
  • Collaborate with engineering teams to deliver high-performance solutions
  • Support mission-critical analytics infrastructure

What they're looking for

  • Apache Spark
  • MapReduce
  • SQL and NoSQL databases
  • Python (Pandas, NumPy, SciPy)
  • Distributed computing
  • Data pipeline development
  • Cloud-based analytics
  • Big data processing

Benefits

  • Salary: $130,000–$270,000 annually based on experience
  • Up to 20% 401(k) contribution vested immediately
  • $3,600 HSA contribution
  • 6 weeks paid time off
  • Fully company-paid medical, dental, vision, and life insurance
  • Short-term and long-term disability coverage
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Captivation Software

Captivation Software builds high-performance computing and secure analytics infrastructure for mission-critical government programs, specializing in HPC testing, enterprise dataflow architectures, and cleared defense systems. The company is hiring senior test engineers, systems engineers, and technical leaders with deep expertise in Linux, cloud infrastructure, and security clearances to validate systems and architect solutions for intelligence community operations.

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Likely interview questions

  • Describe your experience optimizing Spark jobs for performance at scale; what bottlenecks have you encountered?
  • How have you designed data pipelines using MapReduce, and what trade-offs did you consider versus Spark?
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