Artem Lytvynov is a Director of R&D and seasoned engineering leader with 11+ years building and scaling cross-functional teams and data-driven back-end systems from startup MVPs to enterprise products. He has a proven track record founding R&D hubs and leading transformations—shifting siloed front/back teams into full-stack groups, introducing Scrum and quarterly planning, and delivering production-ready architecture for analytics and embedding platforms. Hands-on in Node.js/TypeScript backend development and test automation, Artem has contributed to the popular Cube.js open-source semantic layer by improving core query alias logic and test coverage, reflecting a strong emphasis on code quality and reliability. He pairs technical depth in APIs, micro-frontends and CI/CD with business-savvy product discovery, regulatory risk mitigation, and pre-sales support. Based in Malta, he combines academic training in automation and business administration with entrepreneurial experience as a startup CTO, giving him uncommon fluency across engineering, product and go-to-market execution.
11 years of coding experience
10 years of employment as a software developer
Associate's degree Automation Engineer Technology/Technician, Associate's degree Automation Engineer Technology/Technician at Donetsk Technical School
Master's degree Automation Engineer Technology/Technician, Master's degree Automation Engineer Technology/Technician at Donetsk National Technical University
Master's degree Business Administration and Management General, Master's degree Business Administration and Management General at Donetsk State University of Economics and Trade
📊 Cube’s universal semantic layer platform is the next evolution of OLAP technology for AI, BI, spreadsheets, and embedded analytics
Role in this project:
Back-end Developer & Test Automation Engineer
Contributions:35 releases, 128 reviews, 132 commits in 11 months
Contributions summary:Artem primarily contributed to improving the codebase for the `cube-js/cube` repository, specifically focusing on refining base query alias generation logic. They refactored code and improved code coverage, indicating a focus on code quality. The user also implemented improvements to unit tests, likely for verifying the correct functionality of the core logic. This suggests a solid focus on both back-end functionality and software testing.
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Artem Lytvynov - Director Of R&D at CleanConnect.ai