Ivan Goncharov is a Senior Manager and performance-focused engineering leader with nine years of experience building reliable systems and high-performing teams across JVM, database, and payment-system domains. He combines deep hands-on expertise—evident from contributions to the Prometheus Go client where he optimized core histogram logic and added benchmarks—with a talent for mentoring engineers and scaling infrastructure and processes. At Azul he drove performance measurement and distributed test execution for one of the fastest JVMs, and previously architected performance and observability SaaS platforms and national payment-system pipelines. Ivan’s background in extreme benchmarking (including 1M QPS PostgreSQL work) and pragmatic optimizations has repeatedly translated into measurable cost and performance wins for large enterprises. Based in Guanacaste, Costa Rica, he blends systems-level rigor with a people-first approach to deliver scalable, production-ready solutions.
9 years of coding experience
14 years of employment as a software developer
Oracle Real Application Clusters Administration, Oracle Real Application Clusters Administration at RDTEX
RAC DD4D, RAC DD4D at FORS Training Center
HP-UX System Administration, HP-UX System Administration at HP Learning center
Engineer's degree, Computer Science and Control Systems, Engineer's degree, Computer Science and Control Systems at Bauman Moscow State Technical University
Prometheus instrumentation library for Go applications
Role in this project:
Back-end Developer & Performance Engineer
Contributions:1 review, 6 PRs, 9 comments in 5 years 6 months
Contributions summary:Ivan primarily focused on improving the performance and reliability of the Prometheus client library for Go. Their contributions included refactoring code to use injected time, implementing exponential backoff for atomic operations on floats, and optimizing core functionalities such as the `findBucket` function in the histogram implementation by introducing a linear search for small arrays. The user also added benchmarks to compare the performance of different implementations, showing deep interest in performance characteristics. In addition, they optimized parts of the library by introducing a small delay in the testing process.
Contributions:9 releases, 5 commits, 48 PRs in 1 year 9 months
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