John O'brien
Network Architect at University of Pennsylvania
Narberth, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolJohn O'brien is a Network Architect with 12+ years of experience designing and operating campus and WAN networks for the University of Pennsylvania while running a boutique consultancy, Saltant Solutions. He combines a deep engineering pedigree—from RF and embedded systems to power electronics—with pragmatic platform and software engineering practices, favoring IPv6, DNS, Python, and FreeBSD. Known as a security- and quality-minded technical lead, he designs fault-tolerant systems and precise protocol-driven solutions while mentoring teams through high-stakes incidents. His open-source QA work improving pandas' test stability reflects a detail-oriented approach to reliability that extends beyond networking into software toolchains. Based in Narberth, PA, he balances expedience with long-term governance and standards thinking, enjoying "plumbing of the Internet" problems that reward deep technical dives.
12 years of coding experience
21 years of employment as a software developer
MS, MS at University of Colorado Boulder
Stackoverflow
635reputation
51kreached
15answers
1question
Github Skills (14)
pandas10
python10
testing10
data-science9
data-analysis9
numpy7
graph6
gnu-screen6
namespaces6
dictionary6
xhtml6
xslt6
tree6
lxml6
Programming languages (10)
Github contributions (5)
pandas-dev/pandas
Nov 2013 - Jun 2017
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:14 commits, 4 PRs, 11 comments in 3 years 7 months
Contributions summary:John primarily focused on improving the testing framework and ensuring the stability of the pandas library. They addressed compatibility issues with external libraries like OpenPyXL, writing tests to handle different versions and prevent failures. Additionally, the user incorporated tests related to time zones and data handling, and addressed regressions introduced by dependencies. The user's contributions directly improved the reliability and robustness of the pandas library.
pythondatalabeled-datamanipulationdataframes
neirbowj/munin-ipfw
Aug 2014 - May 2019
Contributions:8 commits, 1 push in 4 years 9 months
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