Matthew Barber
Senior Software Engineer at ASOS.com
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolMatthew Barber is a senior software engineer based in London with 12 years of experience building reliable APIs, automation, and full-stack features across e-commerce and enterprise systems. He currently drives engineering at ASOS and has a strong background modernizing backend stacks—designing .NET Core APIs, adopting Azure DevOps, and automating developer workflows. A pragmatic tester as well as developer, Matthew has made notable open-source contributions to cornerstone Python projects like NumPy, CuPy and Hypothesis, improving array API test coverage and catching subtle edge cases that increase scientific computing robustness. He blends hands-on coding in C#, Python and React with leadership in sprint planning, onboarding and cross-team delivery. His mathematical training (BSc Hons) and early tutoring roles surface in a methodical, data-minded approach to problem solving and mentoring. Colleagues value him for turning fuzzier business requirements into well-automated, testable systems that scale.
12 years of coding experience
7 years of employment as a software developer
A Levels, A Levels at Sackville Sixth FormBachelor’s Degree (Hons) Mathematics with a Year in Industry, Bachelor’s Degree (Hons) Mathematics with a Year in Industry at University of Kent
Stackoverflow
53reputation
22kreached
8answers
3questions
Github Skills (13)
api10
pytest10
python10
cupy10
numpy10
testing10
property-based-testing9
fuzzing9
lib8
statistics6
probability-distribution6
stable-sort6
sorting6
Programming languages (12)
Github contributions (5)
HypothesisWorks/hypothesis
Aug 2021 - Jan 2023
The property-based testing library for Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:89 reviews, 219 commits, 23 PRs in 1 year 4 months
Contributions summary:Matthew primarily contributed to the testing framework of the Hypothesis library. They focused on enhancing the testing capabilities for array-related strategies, including those within the array API. Their work involved porting existing tests from NumPy, developing new tests for indexers, and addressing linting issues. The user also improved the test coverage and made the testing process more robust by addressing potential errors and edge cases, such as subnormal floats.
property-based-testingpythontestingfuzzing
numpy/numpy
Aug 2021 - Oct 2022
The fundamental package for scientific computing with Python.
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:11 reviews, 20 commits, 11 PRs in 1 year 1 month
Contributions summary:Matthew's contributions center on enhancing the test suite for the NumPy library. They focused on improving the test coverage for the array_api module, introducing new tests to validate entry points and edge cases, and modifying existing tests to ensure compatibility across different Python versions and debugging configurations. This work involved modifying test logic and adapting existing tests to align with the specification of the Array API standard. Their efforts directly improved the reliability and robustness of the NumPy library.
pythonscientific-computingnumpy
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