Hey again!
A bit more about me: I don’t believe in hyper-specializing. I admire the Renaissance polymaths like Da Vinci, and I aim to contribute across the data stack: pipelines, forecasts, web apps, or just making things a little less messy. Whatever the work needs.
I care deeply about open-source tools and transparent, reusable solutions.1 I try to reflect that in how I work, focusing on clarity, openness, and long-term maintainability over flashy dashboards or black-box outputs. I didn’t realize how important good code and documentation were until I’d been through a couple of trials by fire, and I don’t want to do that to anybody else.2
Python is the language I work in now. I’ve spent the last couple of years building production Python: CLIs with Typer, services with FastAPI, pipelines on Polars, notebooks in marimo, all managed by uv. The further I get from analytics-only work, the more the ecosystem earns its keep. I came up on R though, and the tidyverse still holds a special place. I would not be the analytical programmer I am without R for Data Science (2e).3 R taught me how to think analytically. Python lets me ship anything I can think of.
I’m also realizing how much software engineering and industrial engineering overlap. Both are about optimizing systems and continuous improvement. Building tools, web apps, and reports in a clean Pythonic style scratches the same itch as designing physical systems. It feels like a natural evolution of how I’ve always wanted to work.
Outside work I’m at concerts, on bike rides, playing guitar, or buried in a side project that may or may not ever get finished. Lately I’ve been getting into self-hosting and homelabs.4 I’m also slowly renovating a 1900s Tudor in Pittsburgh, which has turned out to be its own kind of full-stack project.
If any of this resonates, feel free to reach out.