Oleksii Moskalenko
Senior Software Engineer at Netflix
San Francisco, California, Singapore
Summary
🤩
Rockstar🎓
Top SchoolOleksii Moskalenko is a Senior Software Engineer with 14 years of experience specializing in the intersection of data engineering and data science, currently focused on Machine Learning Platform work at Netflix. He has led feature store initiatives at Gojek and contributed core parts of Feast, the popular open-source feature store (feast.dev) used across leading companies. At Tecton he reimagined batch compute by replacing Spark with DuckDB + Ray and wrote DuckDB extensions to squeeze performance from complex training pipelines. Skilled in Java, Scala, Python, and Go, Oleksii combines backend and DevOps expertise—optimizing ingestion, job coordination, and low-latency online serving at scale. He brings a rare blend of deep systems engineering, production ML deployment experience, and hands-on database internals tuning. Based in San Francisco with academic roots in signal processing and data science, he thrives on squeezing performance from distributed systems and productionizing ML.
14 years of coding experience
13 years of employment as a software developer
Master's degree Data Science, Master's degree Data Science at Ukrainian Catholic UniversityMaster's degree Digital Signal Processing, Master's degree Digital Signal Processing at Dnipropetrovsk National University
Stackoverflow
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Github Skills (11)
javas10
kafka10
feature-store10
devops10
cicd10
java10
data-engineering9
redis9
apache-spark9
python8
machine-learning6
Programming languages (9)
Github contributions (5)
feast-dev/feast
Aug 2020 - May 2022
The Open Source Feature Store for Machine Learning
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:8 releases, 537 reviews, 201 commits in 1 year 9 months
Contributions summary:Oleksii primarily contributed to implementing features for the feature store and its associated job management system. Their work included enhancing the JobCoordinator to handle subsets of FeatureSets and Stores, improving the code for the ingestion pipeline, and optimizing online serving. They also focused on DevOps by restructuring the system to run the JobController as a separate application and creating and improving the CI/CD pipelines.
pythondata-qualitydata-sciencemlmachine-learning
pyalex/feast
May 2020 - May 2022
Feature Store for Machine Learning
Contributions:1077 pushes, 190 branches, 1 tag in 2 years
pythondata-sciencemachine-learningmlopsfeature-store
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