Radu Jica
Backend Engineer at QuantHealth
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolRadu Jica is a backend and data engineer with 11 years of experience building efficient data pipelines and performance-focused systems from Zurich. He has moved between finance and insurance teams as well as startups—most recently joining QuantHealth—bringing practical expertise in production data engineering from roles at Swiss Re, Julius Baer, Doodle and Unit8. Radu favors simplicity and optimization, demonstrated by contributions to the high-performance Weld runtime integrating NumPy arrays for faster analytics. He combines a strong academic foundation in Big Data Engineering with hands-on C++, Python and systems work, and a knack for making complex data transformations leaner and more maintainable. Colleagues describe him as detail-oriented with an eye for low-level improvements that yield real-world performance wins.
11 years of coding experience
7 years of employment as a software developer
Erasmus Exchange First Semester CS, Erasmus Exchange First Semester CS at Universität des SaarlandesInternational Baccalaureate, International Baccalaureate at International School of BerneInformatics, Informatics at Tudor Vianu National College of Computer ScienceJoint Masters Degree in Computer Science Big Data Engineering, Joint Masters Degree in Computer Science Big Data Engineering at Vrije Universiteit Amsterdam (VU Amsterdam)Joint Masters Degree in Computer Science Big Data Engineering, Joint Masters Degree in Computer Science Big Data Engineering at University of Amsterdam
English, German, Romanian
Github Skills (12)
c-language10
rust10
cprogramming-language10
python10
analytics10
data-model10
numpy10
user-data10
data-set10
code-generation9
llvm8
pandas7
Programming languages (10)
Github contributions (5)
weld-project/weld
Mar 2018 - Sep 2018
High-performance runtime for data analytics applications
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 10 PRs, 21 comments in 5 months
Contributions summary:Radu primarily contributed to the Grizzly module, which involves integrating NumPy arrays with the Weld runtime. Their work includes adding support for new data types and array dimensions within the NumPy-Weld conversion process. They modified both C++ and Python code, including the `numpy_weld_convertor.cpp` and `encoders.py` files, implementing encoders and decoders for various data types. These changes enable the efficient use of NumPy data within the Weld framework.
data-analyticsstanforddataanalyticsmachine-learning
radujica/data-analysis-pipelines
Jan 2018 - Jul 2018
Masters Thesis: Exploration of data analysis pipeline optimizations
Contributions:242 commits, 83 pushes, 3 branches in 6 months
data-analysis
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