I'm Zack, a data analyst based in Tulsa, Oklahoma. I spend most of my time turning messy, disconnected information into systems people actually rely on, and the rest of my time thinking about film, games, and whatever's next to build.
I didn't even start in computer science. I went to college for architecture, drawn to how a good building quietly solves a dozen problems at once. An elective in programming, taken on a whim with a friend, changed that. I switched my major to Computer Science not long after and never looked back.
Data itself clicked for me while writing a paper on Ronald Fagin, the researcher behind fourth normal form. One thing led to another and I ended up in an actual correspondence with him about where data storage was headed, a conversation that shaped how I think about this field more than any class did.
Before any of that, I spent about a decade in the restaurant industry, work that put me through college. It's not the background people expect from someone who now lives in SQL and Mixpanel, but it's where I learned to read a room and stay useful under pressure, both of which have mattered more in this career than anything from a textbook.
I picked up a B.S. in Computer Science from Northeastern State University, and in 2018, walked into a small veterinary SaaS company, VetMedux, as its first data hire. There was no analytics function to join. I built one: event architecture, reporting infrastructure, the whole practice, from a blank slate, working directly with the COO in year one.
Eight years, an acquisition by Instinct Science, and a lot of dashboards later, that's still the work I'm best at: showing up somewhere messy and building the thing that makes the mess legible.
A dashboard nobody believes is worthless, no matter how clean the SQL behind it is. I care more about whether a number holds up when someone pushes back on it than whether the chart looks good in a deck. That's shown up in practice as things like correcting a usage-reporting system that had been overstating client activity by roughly three times, or building fraud-detection logic that had to distinguish real abuse from a legitimate power user before anyone would trust it enough to act on.
I also care a lot about transparency, more than almost anything else in how I work. If a number is shaky or the data behind it is incomplete, I say so up front, and I'm specific about how confident I actually am in a given takeaway rather than presenting everything with the same false certainty. I expect the same in return: if something I've shared doesn't sit right with you, tell me. I'd much rather rerun a calculation or revisit an assumption than let a bad number quietly stand.
I'm also genuinely AI-native in how I work, not as a talking point, but because tools like Claude and Claude Code have changed what I spend my time on. The mechanical part of the job, writing boilerplate SQL, drafting documentation, catching my own logic errors, moves faster now. What's left is the part that actually matters: deciding what's worth building, and knowing whether a number is trustworthy enough to act on.
I have a genuine, long-running interest in film, enough that award-season predictability turned into one of the case studies on this site. I keep a running log of what I watch on Letterboxd. Outside of movies, I'm a lifelong video game and board game player, and I spend a fair amount of time cooking, which scratches some of the same problem-solving itch as a good data project.
I was born and raised in the Tulsa area, and that's still home, which is part of why I'm looking for remote roles that let me keep building things worth trusting without having to leave.
Reach out directly, or see the work first.