I spent ~5 years as a soil biogeochemist at Iowa State (DOE-funded CABBI), running field trials and building reproducible pipelines for greenhouse-gas flux and cover-crop nitrogen data. The part I kept loving wasn't the papers — it was the data itself: the QA/QC, the pipelines, the relentless "wait, is this number actually right?"

So I followed that thread. Today I'm a data engineer at LCS, building the infrastructure that lets a team actually trust what its data is telling it.

• A Snowflake + dbt lakehouse — turning ad-hoc SQL into version-controlled, tested, documented transformations
• A self-updating "living documentation" layer so the warehouse explains itself
• A CLI-first dev workflow (WSL/Linux, Git, Neovim) to replace clicking around in web editors

• Reproducible, well-tested analytical pipelines
• Real QA/QC discipline — I assume the data is wrong until it proves otherwise
• Statistical reasoning that goes past "the dashboard said so"
• Years of wrangling messy, real-world data (mostly in R)

Currently leveling up on dbt, dimensional modeling, and the modern data stack.

• Running an offline-capable local-AI homelab — CachyOS on a Ryzen + RTX 5070 TI (5090 soon) box, self-hosting LLMs to see how far I can get without the cloud
• A Debian NAS quietly humming along with Nextcloud, Jellyfin, and Tailscale
• 🔴 Liverpool FC through thick and thin — You'll Never Walk Alone
• 🏃 Training for Tripple Bypass ride in July and an October marathon (and yes, the splits live in a spreadsheet)
• 🌽 Iowa-grown, Polk County

• 🐙 GitHub — you're already here