Kellen Dye is a Senior Data Engineer with 11 years of experience building and optimizing large-scale data pipelines, currently driving data engineering efforts at Spotify in New York. He brings deep backend expertise in Apache Beam/Google Cloud Dataflow, contributing to the well-known scio open-source project by modernizing BigQuery integrations, improving skewed joins, and implementing a mutable scalable bloom filter. His background spans senior engineering roles across startups and media-tech firms, combining practical production work with research-informed MSc training in computer science from Uppsala. Trained originally in both fine arts and computer science, he blends creative problem-solving with rigorous system design to improve performance and maintainability in complex data systems. Recruiters should note he prefers compensation included in initial outreach.
11 years of coding experience
11 years of employment as a software developer
University of California, San Diego
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Uppsala University
A Scala API for Apache Beam and Google Cloud Dataflow.
Role in this project:
Back-end Developer
Contributions:2 releases, 258 reviews, 33 commits in 2 years 8 months
Contributions summary:Kellen primarily focused on maintaining and improving the existing codebase. They addressed deprecated methods in the BigQuery integration, updating the API usage for compatibility. Furthermore, the user refactored the code related to skewed joins by wrapping internals in transforms and adding side input support to SMB transforms, contributing to performance improvements. They also implemented a mutable scalable bloom filter.
Contributions:10 pushes, 3 branches in 5 years 11 months
beamapachebig-datajavaapache-beam
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