Bulat Yaminov
Software Engineer at Uber
Randstad, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolBulat Yaminov is a Staff Software Engineer with 12 years of hands-on experience across backend, data engineering, data science, frontend and mobile development, currently based in the Randstad, Netherlands and working at Uber. He has a strong track record leading teams and rebuilding systems for performance, including multi-order-of-magnitude backend refactors and architecting modular, pluggable platforms for scalable product development. His background spans building cloud-native microservices, ML pipelines and DevOps-focused SaaS on Kubernetes and GCP, as well as applied machine learning in product features. Bulat contributes to open-source documentation for high-performance data tooling (Vaex), reflecting a focus on making big-data tools more accessible. He pairs deep technical breadth—from Scala/Java backends and Spark to Python data science—with pragmatic leadership in hiring, process improvement and delivery. Notably, his career mixes early mobile and ECM expertise with recent large-scale data and performance work, showing both adaptability and long-term technical ownership.
12 years of coding experience
10 years of employment as a software developer
Master, Computer Science, Master, Computer Science at Saint-Petersburg University of Information Technologies, Mechanics and Optics
Russian, English, Japanese, Dutch
Github Skills (11)
user-manual10
jupyter-notebook10
datatable10
basics10
learn-ruby-on-rails10
python10
tabular10
documentation10
data-visualisation9
data-visualization9
data-visualizations9
Programming languages (7)
Github contributions (5)
vaexio/vaex
Jan 2020 - Apr 2020
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Role in this project:
Technical Writer
Technical WriterContributions:8 reviews, 10 commits, 8 PRs in 3 months
Contributions summary:Bulat primarily contributed to improving the documentation of the vaex library. Their work involved fixing typos, clarifying explanations, and updating tutorials within the documentation files, particularly in the Jupyter Notebooks. The changes indicate a focus on making the library more accessible and user-friendly through comprehensive and accurate documentation. This is evident by edits across various sections, including the API, and examples, which are designed to help users understand Vaex's features and functionalities.
pythonhybrid-corearrowrowsbigdata
xebialabs-community/xl-release-stress-tests
Apr 2015 - Oct 2017
Contributions:4 PRs, 40 pushes, 8 branches in 2 years 6 months
testingxl-release
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