I build and own the seams a platform can't run without.
Backend engineer working the hard parts of a system — keeping data consistent across services that don't trust each other, and event-driven pipelines reliable under load. For three years, on a healthcare platform serving thousands of clinics, where correctness and uptime aren't optional.
Anatomy of a
twelve-second request.
One booking API. Five moves from unusable to under a second. This is how I think when something is slow — and why the fix that mattered was in the architecture, not the code everyone was staring at.
A patient clicks “book.” Twelve seconds pass. That isn't slow — it's broken.
Slot availability across four clinical verticals, recomputed server-side on every request. The team's instinct: the scheduling algorithm must be too heavy. It was the intimidating code, so it felt like the slow code.
I don't get an opinion until I've measured. So I put a clock on the hot path.
Don't instrument to confirm a hypothesis — instrument to partition the time. Divide the request into regions, measure each, and the bottleneck announces itself.
The timers didn't point at our algorithm — they pointed underneath it. A reputable scheduling library, built on moment.js, was materializing a 1,440-entry array per schedule per date and throwing it away. At real request shapes that's ~1.3M short-lived objects per request — pure garbage-collection pressure on the hottest path in the product.
Replace the library — and lean on an architecture built to make exactly this change safe.
Replace the library.
A native Set-intersection engine: a slot is valid iff every minute of its window is free in every schedule's set. Equivalence proven against the library's own source. ~1.3M → ~0 allocations.
The design that contained it.
A per-vertical strategy/factory on a worker-thread pool — CPU-bound compute off the event loop, and a new vertical plugs in without touching the core.
The architecture is why a large performance problem had a small, surgical fix — one module, not a rewrite.
Team's verdict: “very, very fast.” The lesson I keep relearning — the intimidating code is rarely the cost, and a clean architecture is what turns a scary performance problem into a one-module fix.
// that fix wasn't luck
It's a method. The same five ideas run under every system I build.
Instrument before you touch anything. Design for the failure first. Keep the core clean and let the mess live at the edges. The story above was just principle one, doing its job.
Measure before optimizing.
The bottleneck you can prove is rarely the one you assume.
Prefer reliability over elegance.
Design the failure modes first; a fast wrong answer is still wrong.
Hide variability behind stable interfaces.
A stable core is a compounding asset. Absorb the mess at the edges.
Abstract only after patterns emerge.
A premature framework taxes assumptions you haven't validated.
Own production.
Releases, incident response, and data repair are engineering too.
// method meets scale
The same thinking, measured against a platform serving thousands of clinics.
A number only counts if you can say where it came from. Each of these is measured on beta or read from the production dashboard — sourced, not rounded into a slogan.
Booking availability, after finding the bottleneck the team assumed was the algorithm.
measured · betaMessages retained on the durable Kafka logging backbone I built — the fix that ended data-loss-under-load.
prod dashboardHeterogeneous external systems — API, database, or UI only — normalized onto one canonical model.
code-verifiedActive clinics depending on these systems, across four clinical verticals that each break a different assumption.
platform scale// the record
The work, in two collections.
Not summaries — full engineering narratives. Production Systems prove senior ownership inside a team; Independent Projects prove the same muscles solo, from zero. Each opens with the problem that forced the decision.
More open, more transparent, more technical — I own every part, so I can show every part.
A self-hosted platform (Docker · Caddy · per-project DB isolation · encrypted off-box backups) runs these — and more experiments are in progress.
If the hard part is keeping systems consistent and fast, that's the work I do best.
The fastest way to understand how I work is a case study. The fastest way to reach me is email.