Andrew Martin is a Staff Machine Learning Engineer at Spotify with 11 years of experience building data and ML infrastructure in New York. He progressed through senior engineering roles at Spotify focused on scalable back-end and DevOps work, shipping production data pipelines and improving release and build processes. His open-source contributions to the widely used spotify/scio project show hands-on expertise with Apache Beam/Dataflow, Hadoop integration, and HDFS reliability fixes. Colleagues rely on him for bridging data engineering and ML needs, turning infrastructure gaps into repeatable, automated solutions. He holds a Computer Science degree from McGill and brings a pragmatic mix of research-era curiosity and production-grade engineering discipline. An often-overlooked strength is his attention to release hygiene and build tooling, which reduces operational friction across teams.
11 years of coding experience
11 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at McGill University
A Scala API for Apache Beam and Google Cloud Dataflow.
Role in this project:
Back-end & DevOps Engineer
Contributions:178 commits, 176 PRs, 436 pushes in 3 years 1 month
Contributions summary:Andrew primarily focused on making improvements to the release process and build configuration of the project. Their contributions included adding themselves to the developer list, setting and updating the project's version number, and fixing HDFS authentication issues. Additionally, they addressed empty SCollection writes to the HDFS Sink, which highlights their work on core data processing and storage components. The user also introduced support for multiple paths for the Hadoop dist cache, indicating their work on infrastructure.
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Andrew Martin - Staff Machine Learning Engineer at Spotify