Deep Tavker is a Performance Engineer at Flow Traders with a decade of experience applying mathematics, computational physics and trading systems expertise to low-latency financial technology. An IIT Bombay alumnus with research stints in algorithmic trading and computational physics, he has moved from trading operations into performance-focused engineering roles where optimization and reliability are paramount. He contributes to open-source scientific tooling—most notably modernizing test infrastructure for a Python Smoothed Particle Hydrodynamics framework—demonstrating pragmatism in improving code quality and CI. Based in Amsterdam, he combines deep technical rigor with curiosity-driven hobbies like Formula 1, keyboard playing and speedcubing, reflecting a temperament for speed, precision and pattern recognition.
A framework for Smoothed Particle Hydrodynamics in Python
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:35 commits, 6 PRs, 9 comments in 8 months
Contributions summary:Deep primarily focused on migrating the testing framework from `nose` to `pytest`. This involved refactoring existing test files, replacing deprecated plugins, and updating import statements to align with `pytest`. They also introduced the use of `pytest.skip` and `pytest.importorskip` for conditional test execution and updated the project's requirements. Additionally, the user added tests for the `get_files` function and modified the `setup.py` file to enable linetracing.
Contributions:111 commits, 48 pushes in 2 years 11 months
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