Performance Engineer – DB Internals – CPU Architecture

As a Lead Performance Engineer, you will be working on complex distributed systems, databases and large clustered datasets. You will lead efforts to optimize overall system and product performance. You’ll work with the Development, QE (Quality Engineering) and Product teams to deliver high quality initiatives for all our products throughout their development life cycles.

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7+ years

About you

  • 7+ years of hands-on performance benchmarking experience with distributed systems
  • Strong experience on performance testing tools –open source performance tool, jmeter, usage to distributed messaging and brokers for testing
  • Excellent programming skills (C/C++/Java/Python)
  • Strong understanding of CPU architectures (e.g., multicore, hyper threading, and L1/L2 cache)
  • Strong understanding of memory management architecture (e.g., virtual memory, NUMA, TLB, and Transactional Memory)
  • Strong understanding of file systems (e.g., ext3, ext4, ZFS) architectures and trade-offs
  • Strong Linux/Unix skills
  • Ability to identify bottlenecks and limitations in software and hardware architectures
  • Strong understanding of operating systems
  • Strong understanding of network and application protocols
  • Experience with database internals is a strong plus

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  • Handle multiple performance testing projects working on a global delivery model with US and India resources.
  • Data Visualization – prepare test reports
  • Coordinate with the Dev / QA / OPS teams to review and document requirements, execute performance tests and troubleshoot/resolve (blocking) issues.
  • Analyze and assess Performance, Scalability, Resiliency, Serviceability, and Availability testing needs of the system under test.
  • Recommend appropriate testing approach to evaluate the above properties of systems and applications.
  • Design and conduct tests with complex workloads and data shapes to expose potential bottlenecks and failure points.
  • Analyze performance results to identify system inefficacies and suggest optimizations to address the same.
  • Look for ways to improve user experience and system efficiencies.
  • Innovate solutions to improve performance engineering effectiveness
  • Conduct technical presentations to educate teams on how to improve system performance
  • Analyze metrics and trends to glean opportunities for performance improvements of our systems

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