example production project: credential mapping for reaccreditation, a critical data project for a large institution

Python has long played a role in my work. Perhaps its most visible place is as a go-to tool for explorations in Jupyter Notebooks, which I use as a home base for exploring datasets, querying ideas, examining analytics for our applications, machine learning experiments, or for any other data-centric task. Pandas and numpy are essential here.

I have written and deployed many microservices based on flask (usually under waitress, then proxied by nginx) for internal APIs to serve applications; many of these additionally used GraphQL on the endpoint. In other cases, Python takes on the task of mapping very large relational databases (Oracle at the university) into object structures, and then somtimes into a graph form or dumped into something like MongoDB for further use, often on a nightly schedule.

I have also contributed heavily to integrating Python-based data work and data science into workflows that rely on SAS, contributing to major open-source initiatives in this domain.