Menno Van Rahden
Senior Software Engineer at Computely.io Technologies
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolMenno Van Rahden is a Senior Software Engineer based in Munich with nine years of experience designing and operating cloud-native, event-driven systems for enterprise and automotive environments. He builds serverless AI-enabled applications for Bayer’s "AI-driven Farming" initiative and has driven end-to-end testing and remote-agent tooling for Mercedes-Benz’s MB.OS, blending strong DevOps practice with systems thinking. A co-founder of an IoT management startup and a longtime tutor in algorithms and object-oriented engineering, he pairs hands-on backend development with a deep interest in organizational theory and information management. Menno is an active open-source contributor who has improved testing, race-condition handling, and provider support in the lightweight Go configuration library koanf, demonstrating attention to code quality and reliability. His background in industrial engineering and a thesis on AI’s organizational impact give him a rare combination of technical depth and strategic perspective.
9 years of coding experience
12 years of employment as a software developer
Master of Science, Engineering/Industrial Management, Master of Science, Engineering/Industrial Management at Technische Universität Braunschweig
English, German
Github Skills (11)
testify10
configuration-management10
go10
go-packages10
testing10
filesystem9
fs9
error-handling8
toml7
yaml7
json7
Programming languages (9)
Github contributions (5)
knadh/koanf
Jun 2021 - Jun 2021
Simple, extremely lightweight, extensible, configuration management library for Go. Supports JSON, TOML, YAML, env, command line, file, S3 etc. Alternative to viper.
Role in this project:
Backend & Test Automation Engineer
Backend & Test Automation EngineerContributions:2 reviews, 8 commits, 4 PRs in 6 days
Contributions summary:Menno primarily contributed to improving the testing infrastructure and code quality of the koanf configuration management library. They updated dependencies, including testify, and refactored test arrangements to unify temporary directory usage and improve efficiency. Furthermore, the user addressed race conditions in the file watching feature, implementing a channel and WaitGroup for synchronization. Additionally, they added an fs.FS provider and improved error handling in tests.
environment-variablesconfiguration-fileconfig-loaderargument-parsingenv
mvrahden/learning-agents
Mar 2018 - Jul 2021
Reinforcement Learning Agents learn with Deep-Q-Learning Network to collect superior and avoid inferior items.
Contributions:76 commits, 2 PRs, 68 pushes in 3 years 3 months
deep-q-learning-networkdeep-reinforcement-learningsimulationavoiddeep-q-learning
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