Stephen Boesch
Python Data Engineer And Data Scientist
Renton, Washington, United States
Summary
👤
Senior🎓
Top SchoolStephen Boesch is a Python-focused data engineer and data scientist with 11 years of experience building production ML pipelines, ETL systems, and large-scale Spark workflows across enterprise and startup environments. He blends hands-on expertise in pandas, PySpark, Scala/Spark and feature engineering with practical experience in computer vision, audio signal processing, recommendation systems, anomaly detection, and forecasting. Stephen has supported mission-critical workloads for companies like Apple and Intuit and now enables streaming and batch logistics solutions in Azure Synapse at Blueprint. His background includes performance-tuning distributed systems (Spark/Hadoop) and contributions to open-source Spark projects such as SparkNet, showing a comfort with both Scala backend changes and practical tooling enhancements. Comfortable across cloud stacks (AWS, Azure) and infrastructure-as-code, he often bridges research-grade models and production deployment. Based in Renton, WA, he combines deep legacy systems experience (Java/Oracle) with modern data science practices learned during an MS in Computer Science (Data Science).
11 years of coding experience
24 years of employment as a software developer
Bachelor of Science (BS) Electrical Engineering, Bachelor of Science (BS) Electrical Engineering at Purdue UniversityMaster of Science (M.S.) Computer Science - Data Science, Master of Science (M.S.) Computer Science - Data Science at University of Illinois Urbana-Champaign
italian, spanish
Github Skills (6)
testing10
spark10
back-end-development10
scala10
code-formatting9
command-line-interface7
Programming languages (19)
Github contributions (5)
amplab/SparkNet
Feb 2016 - Feb 2016
Distributed Neural Networks for Spark
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 4 PRs, 46 comments in 1 day
Contributions summary:Stephen's primary contributions involve modifying Scala code within the SparkNet project, focusing on removing unnecessary code elements, such as return statements and semicolons, and fixing code formatting. They made changes across multiple files, including loaders, libraries, and testing applications, demonstrating involvement in various project components. The user also added functionality to allow specifying the Spark master on the command line, extending the flexibility of the CifarApp. Finally, the user reverted a previous commit, indicating some level of code management and decision-making.
neural-networksmachine-learningsparkscaladistributed
javadba/spark
Feb 2015 - Oct 2015
Contributions:9 pushes, 2 branches in 7 months
apachebig-datasparkscalaapache-spark
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