About

My real name is Sergio (AKA Keko—that’s what my friends and family call me). I grew up in the early 2000s doing what every respectable 90’s kid did: turning LimeWire downloads into functioning video games by brute force, WinRar, and questionable ethics.

I didn’t call it “engineering” back then. I called it “making it work so I can play Age of Empires II tonight.” But that was the pattern: I liked systems. I liked breaking them open. I liked putting them back together.

Then someone (probably a teacher) fed me the classic lie: “You need to be a math genius to program.” So instead of studying computer science, I drifted into “serious” careers—psychology, medicine, biology—because I wanted a challenge and I didn’t know where else to point the ambition.

By the time I hit bioinformatics, the illusion broke. I was in R, handling genomic data, building a heatmap from FASTA files and a published pipeline… and I realized something uncomfortable:

I wasn’t “bad at programming.” I’d just been avoiding it.

From there I took the hustler route: customer-facing roles, support, onboarding, internal IT—learning how real systems break and how real people panic when they do.

I got rejected from a TAM role after seven rounds. No satisfying explanation. Just a polite “no.” It stung in the way that makes you either spiral or sharpen. I chose sharpen. I went all-in on SQL, case shapes, and the hard skills that don’t care about credentials.

Now I’m at Stripe in TechOps, building and running pipelines that monitor transactions, detect bad actors (fraud/card testing), and support incident analysis and auth-rate optimization. The work is basically: take messy reality, turn it into signals, and make those signals show up fast enough to matter.

Here’s the thing that makes me different (and yes, it’s annoying):

I’m a scientist at heart. I don’t worship elegance. I worship repeatable results. If we can’t reproduce it, verify it, and explain it step-by-step, the “beautiful” solution is just performance art.

I’d rather ship code that’s a little ugly but never surprises you—clear, logged, debuggable—like an AK-47 of software: not precious, just dependable.

Next, I’m aiming for roles where that mindset compounds: Data Engineering, Data Science, and Applied AI—especially around payments, orchestrators, and the not-fun-but-critical parts of financial systems: settlement, reconciliation, detection, and control loops.