Joel Becker is a data engineer with a decade of experience building scalable data assets and pipelines at Shopify, where he advanced from junior to senior roles. He combines practical production engineering with a research-rooted mindset from his time at Waterloo's NetLab, contributing open-source record linkage tooling (including a longest common substring algorithm) that supports large-scale collaboration network research. His interdisciplinary Bachelor of Knowledge Integration underpins work that bridges technical rigor with social and ethical context, informing research on team diversity, supervisory effects, and online responses to global crises. Based in Kitchener, he brings a rare blend of hands-on pipeline engineering, open-source development, and social-science-informed curiosity to data problems.
10 years of coding experience
11 years of employment as a software developer
Bachelor of Knowledge Integration Joint Honours Knowledge Integration and Psychology Computer Science Minor Dean's Honours List, Bachelor of Knowledge Integration Joint Honours Knowledge Integration and Psychology Computer Science Minor Dean's Honours List at University of Waterloo
A powerful and modular toolkit for record linkage and duplicate detection in Python
Role in this project:
Data Scientist
Contributions:12 commits, 2 PRs, 13 comments in 14 days
Contributions summary:Joel primarily focused on implementing and testing a longest common substring (LCS) similarity algorithm within the recordlinkage library. They added the algorithm's core implementation, integrated it into the comparison framework, and created comprehensive tests covering various normalization methods and edge cases. Furthermore, the user documented the source code for the implemented LCS algorithm.
Contributions:2 PRs, 47 pushes, 2 branches in 5 years 10 months
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