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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 design and maintain high-performance distributed data processing solutions. The role requires expertise in big data technologies and a strong background in analytics development for classified government programs.

What you'll do

  • Develop and maintain high-performance analytic solutions processing large data volumes
  • Write and optimize distributed computing jobs using Spark and MapReduce
  • Design and implement SQL/NoSQL database schemas for analytics workloads
  • Build data processing pipelines using Python scientific libraries (Pandas, NumPy, SciPy)
  • Collaborate with cross-functional teams to deliver mission-critical analytics solutions
  • Ensure code quality and performance optimization for cloud-based systems

What they're looking for

  • Apache Spark
  • MapReduce
  • SQL and NoSQL databases
  • Python (Pandas, NumPy, SciPy)
  • Distributed computing
  • Data pipeline development
  • Cloud-based analytics
  • Software engineering best practices

Benefits

  • Salary range $130,000–$270,000 based on experience
  • Up to 20% 401(k) contribution, vested immediately
  • $3,600 annual HSA contribution
  • 6 weeks paid time off
  • Fully company-paid medical, dental, vision, life, and disability insurance
  • Above-market hourly rates
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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 large-scale data processing and the performance improvements you achieved.
  • Walk us through a complex analytics pipeline you built using MapReduce or Spark—what challenges did you encounter?
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