About
Technologist, futurist, data purist.
I lead with geography. Almost everything an organization tracks happens somewhere, and that shared location is the thread I use to connect the systems they already have, and make sense of it all.
The short version
Most teams already have the data they need. The trouble is that it lives in systems that were never built to talk to each other.
My work lives at the seams between systems, the places where a SharePoint list, an ArcGIS layer, a road network, and a labor-market dataset all describe the same reality but never quite meet. I build the bridges: geospatial analytics that turn location into decisions, automations that erase manual handoffs, and custom tooling that fuses disparate sources into one dependable system.
I care about doing it honestly, results that are reproducible, physically sensible, and resilient to the messy reality of real organizational data. I am not after a clever demo. I am after a connected source of truth that people can act on, long after I have handed it over.
How I work
These are the principles I have earned the hard way, working on real data rather than in theory.
Run it, don’t just read it
The most expensive defects do not show up in code review. They surface the moment a tool meets real data, so I budget for iterative execution on representative datasets from the start.
Verify against primary sources
Behavior gets confirmed against the vendor’s own documentation, not memory. Several of my best fixes came straight from reading the docs everyone else skips.
Own the data structures
The recurring source of fragility is the input. Copying messy sources into a clean representation I control removes whole classes of bugs at once.
Prevent silent wrong answers first
A crash gets noticed right away, but a result that looks right and is wrong quietly feeds a bad decision. I design against the quiet failures first, because the loud ones take care of themselves.
Build for the messy reality
Joins, blank parameters, mixed coordinate systems, nested layers, production data is never clean, so the tool handles all of it by default.
Make it portable and reusable
Self-contained, shareable, zero-dependency where possible, so the work keeps running on someone else’s machine, in another region, a year from now.
Toolkit
The stack I reach for.
Geospatial
Arcade
arcpy
Network Analyst
StreetMap Premium
H3 hex grids
Automation
Engineering
Python
NumPy
Data modeling
HTML / CSS
Git & GitHub
Domains
Credentials · a live roadmap
Earned, in progress, and queued.
I treat my own development like the systems I build, transparent and versioned. Here’s exactly where my credentials stand today, and what’s next. This board updates as each one lands.
Building the credential set deliberately, industry-recognized certifications alongside continued learning.
Built for what's next
Four traits I sharpen every day: ALIC.
Technology keeps moving, and the AI era moves fastest of all. These are the traits that keep me valuable through every shift, and I train them deliberately.
Adaptability
When the platform, the dataset, or the constraints change, I re-orient fast and deliver in the environment that exists rather than the one I'd prefer.
Learning
There is always a certification track in progress, a primary source open, or a new tool under my hands. Staying current is not a phase I am going through, it is simply how I work.
Ideation
I tend to see the connection others miss between systems, datasets, and disciplines, and I turn it into a concrete concept we can actually build.
Change
Ideas are easy and enacted change is rare. I carry solutions all the way through adoption: the tool gets used, the workflow gets switched, and the decision gets made.
Let’s connect your systems.
If your data is scattered across tools that don’t talk to each other, that’s exactly the kind of problem I want to hear about.