David Van Der Leij
Development Tooling Engineer II at Adyen
Amsterdam, North Holland, Netherlands
Summary
👤
Senior🎓
Top SchoolDavid Van Der Leij is a Development Tooling Engineer II based in Amsterdam with 11 years of experience building and operating scalable developer tools and platforms. He specializes in cloud-native tooling—deploying JetBrains IDE services and telemetry pipelines on Kubernetes—and rewrites critical CI and local developer utilities in Go to improve reliability for hundreds of engineers. His background spans JVM and systems work (Java, Kotlin, Python) and includes performance-focused migrations (e.g., a Python-to-Go seating algorithm) and ecosystem contributions to Java static analysis tooling. Notably, his MSc thesis produced an automated refactoring pipeline that became a conference paper, reflecting a blend of research-driven insight and production delivery. Colleagues rely on him to diagnose and harden build/pipeline issues and to make large monorepos and IDE fleets more scalable and observable.
11 years of coding experience
4 years of employment as a software developer
Information Technology, Information Technology at Metropolia University of Applied SciencesBachelor of Science (BSc.) HBO-ICT Software Engineering, Bachelor of Science (BSc.) HBO-ICT Software Engineering at HanzeMaster of Science - MSc Computer Science, Master of Science - MSc Computer Science at Delft University of TechnologyComputer Science, Computer Science at Clarkson University
Dutch, English
Stackoverflow
31reputation
2kreached
1answer
4questions
Github Skills (13)
javas10
static-analysis10
computer-engineering10
java10
junit9
testing8
gpio6
go6
javascript6
buffered-reader6
google-sign-in6
angular6
facebook6
Programming languages (13)
Github contributions (5)
mauricioaniche/ck
Sep 2020 - Oct 2020
Code metrics for Java code by means of static analysis
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 17 commits, 4 PRs in 1 month
Contributions summary:David primarily contributed to the project by modifying and adding code related to the codebase metrics analysis. They removed dependencies, refactored code to use newer APIs, and added functionality to analyze individual files. These changes included adding utility methods and a test case for individual file analysis, which expanded the features of the project. The user also merged branches to incorporate optimized dependencies and master branch changes.
code-metricscodeclimatestatic-code-analysismeansmetrics
refactoring-ai/Refactory
Sep 2020 - Jan 2023
This project implements the recommendation of Extract Method refactoring opportunities using Machine Learning on GitLab merge requests.
Contributions:2 releases, 1 review, 73 commits in 2 years 4 months
opportunitiesmethodrecommendationgitlabmachine-learning
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