John Heasly
Data Engineer
Wildwood, Missouri, United States
Summary
👤
Senior🎓
Top SchoolJohn Heasly is a data engineer with 12 years of experience who specializes in turning messy, public-facing information into reproducible datasets that drive investigative and accountability journalism. At USA TODAY he automates scraping, ETL, and monitoring pipelines with Playwright, Python, GitHub Actions, and Google Cloud, and applies Pandas, SQLite, and Vertex AI to clean, classify, and surface story leads. He builds tooling that helps reporters scale workflows and track changes over time, marrying newsroom priorities with production-grade engineering. An active open-source contributor, he has enhanced the agate Python library with statistical functions and stronger tests, reflecting a focus on human-friendly data tooling. Based in Wildwood, Missouri, he brings a pragmatic, tenacious approach shaped by a background that includes an MSJ from Northwestern and time in the U.S. Navy. Not obvious from the resume: he combines newsroom empathy with backend rigor to prioritize reproducibility and auditability in every pipeline he builds.
12 years of coding experience
M.S.J., M.S.J. at Northwestern UniversityBachelor of Arts - BA, Bachelor of Arts - BA at University of Notre Dame
Stackoverflow
978reputation
70kreached
7answers
1question
amazon-s3
top-5%
amazon-web-services
top-5%
Github Skills (18)
lib10
python10
data-analysis10
testing9
unit-testing9
amazon-web-services9
amazon-s39
unit-test9
data-structures8
data-structure8
ubuntu6
django-models6
django-forms6
virtualenvwrapper6
virtualenv6
Programming languages (13)
Github contributions (5)
wireservice/agate
Apr 2014 - May 2014
A Python data analysis library that is optimized for humans instead of machines.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:20 commits in 16 days
Contributions summary:John primarily focused on enhancing the `agate` library by introducing new features and refining existing functionalities. Their contributions included implementing a `percentile` function, adding a `mad` method, and integrating a `z-scores` function to the codebase. Furthermore, the user worked on test suite improvements by updating various test files with expanded test cases, and addressing potential issues.
data-analysispythondata-sciencemachinesmachine-learning
registerguard/discovery
Mar 2015 - Oct 2018
Contributions:105 commits, 92 pushes, 5 branches in 3 years 8 months
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