Carol Froehlich
IT Specialist at IBM
Charlotte, North Carolina, United States
Summary
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SeniorCarol Froehlich is an experienced IT specialist with over three decades at IBM, blending deep mainframe operations and SAP Basis expertise with long-standing leadership in SAP security. She has managed day-to-day production support, upgrades, and conversions from MVS/JES3 and SAP R/2 to R/3, and has led teams of 10–12 as the primary customer-facing security lead on large entertainment and pharmaceutical contracts. Her current role emphasizes security leadership over predominantly offshore teams, ensuring operational continuity and governance. In open source, she contributed backend and DevOps improvements to the well-regarded neuroimaging workflow engine nipype, adding resource-aware multiprocessing, enhanced logging/monitoring, and log JSON conversion—demonstrating a practical focus on reliability and observability. Known for hands-on problem solving across lifecycle and security domains, she pairs institutional IBM experience with pragmatic engineering contributions to community projects. Based in Charlotte, NC, she brings institutional knowledge of mission-critical systems and a proven track record of translating complex operational needs into maintainable solutions.
11 years of coding experience
Github Skills (14)
multiprocessing10
multi-process10
python-multiprocessing10
workflow-engine10
dataflow-programming10
resource-management10
dataflow10
python10
neuroimaging10
testing9
json8
devops8
big-data7
data-science6
Programming languages (3)
Github contributions (5)
nipy/nipype
Sep 2015 - Oct 2016
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:26 commits, 8 PRs, 2 comments in 1 year
Contributions summary:Carol primarily contributed to the development of a resource-aware multiprocessing plugin within the neuroimaging workflow engine. Their work involved implementing features for managing memory and thread usage, enhancing the plugin's ability to efficiently utilize system resources. They also addressed testing issues and created a JSON converter for the log files, and they modified the callback function to improve the logging system. Furthermore, the user made significant improvements to the logging and monitoring, and refactored the code to handle the errors on the run.
workflowneuroimagingpythondata-scienceworkflow-engine
carolFrohlich/carolFrohlich.github.io
Mar 2018 - Jan 2025
Contributions:17 pushes in 6 years 11 months
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