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Captivation Software

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

Annapolis Junction, MD$130k–$270kmidAdded 3 days ago

About this role

Captivation Software seeks a mid-level cloud-based analytics developer to design and maintain high-performance data processing solutions handling large-scale distributed datasets. You'll leverage big data frameworks and scientific computing libraries to build innovative analytics systems supporting national security missions.

What you'll do

  • Develop and maintain high-performance analytic solutions for large-scale data processing
  • Design distributed computing solutions using Spark and MapReduce frameworks
  • Build SQL/NoSQL database schemas and query optimization for analytics workloads
  • Implement data transformation pipelines using Pandas, NumPy, and SciPy
  • Collaborate with engineering teams to deliver timely solutions meeting customer requirements
  • Optimize performance of analytics systems handling heavy data volumes

What they're looking for

  • Apache Spark
  • MapReduce
  • SQL and NoSQL databases
  • Pandas, NumPy, SciPy
  • Distributed computing
  • Data pipeline development
  • Cloud-based analytics
  • High-performance optimization

Benefits

  • Annual salary $130,000–$270,000 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, 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 to handle very large datasets and the techniques you used to improve performance.
  • Walk us through a complex data pipeline you've built using Pandas/NumPy/SciPy and explain how you handled data quality and edge cases.
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