Rok Mihevc
Committer at Datatart
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolRok Mihevc is a committed back-end engineer and Apache Arrow committer with 16 years of experience applying a physics-trained, data-first mindset to large-scale data tooling and ETL pipelines. Based in Amsterdam, he has driven memory-allocation and sparse tensor work in Apache Arrow—contributing production-grade features like subpools, cython wrappers, and NumPy matrix serialization for a widely used columnar in-memory format. His freelance and industry roles span cloud ETL (AWS, Athena, Airflow), ML workflows (Keras/TensorFlow on GCP/TPU), and domain-specific data lifts such as bootstrapping a national sinkhole catalog from raw LiDAR. Comfortable moving between research and production, he combines low-level systems work with pragmatic data engineering to make high-performance data interchange and analytics accessible.
16 years of coding experience
8 years of employment as a software developer
Erasmus exchange year Physics, Erasmus exchange year Physics at Technische Universität BerlinDiploma Theoretical Physics, Diploma Theoretical Physics at University of Ljubljana, Faculty of Mathematics and Physics
Slovenian, English, German, Croatian
Github Skills (11)
apache-arrow10
data-structures10
memory-management10
c-language10
cprogramming-language10
python10
data-structure10
numpy9
serialization9
data-serialization9
parquet7
Programming languages (10)
Github contributions (5)
apache/arrow
Jun 2018 - Jan 2023
Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1483 reviews, 77 commits, 135 PRs in 4 years 8 months
Contributions summary:Rok's contributions primarily involve the implementation of new features and enhancements related to memory allocation and the creation of data structures within the Apache Arrow project. Their work includes developing subpools for memory management, creating cython wrappers for sparse tensors, and adding methods for converting sparse tensors to dense tensors. They also worked on implementing NumPy matrix serialization, which involved adding new serialization handlers and support for various data types. They have also made advancements in adding support for various sparse tensor formats.
memorymulti-languagetoolboxacceleratedarrow
rok/datatart.com
May 2017 - Apr 2024
Contributions:6 pushes, 1 branch in 6 years 11 months
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