Scale & Reliability
Your system works until it doesn’t. I load-test it, trace the bottlenecks, fix the data model and the queues, and put SLOs, backpressure, and autoscaling in place so growth stops being scary.
- It falls over under real traffic
- Latency is creeping up and nobody knows why
- On-call is exhausting your best people
- A big launch or season is coming
What I do
- Load testing & profiling
- Query, cache & queue optimization
- SLOs, alerting & dashboards
- Autoscaling & backpressure
- Incident & on-call hardening
What you get
- A measured before/after report
- Tuned services & data layer
- SLOs + dashboards you keep
- A capacity & cost model
- A leaner on-call rotation
Cut checkout p99 latency 85% before a 9× launch
A streaming pipeline that ingests 2B events/day
Common questions
Most work is fixed-scope or capped: after a discovery call we agree on a written scope, milestones, and a price before any code is written. Ongoing work runs as a monthly retainer with reserved days. You always know the number up front — no hourly meter running in the background.
Fully remote. I’m based in Colorado (US Mountain, UTC−7) and overlap comfortably with US time zones and European mornings. I work async-first with a weekly demo and written updates, so progress is visible without standing meetings.
No. I’ll work in your stack — I’m language-agnostic on principle. My “stack” page is where I’m fastest, not a list of requirements. For greenfield work I’ll recommend deliberately boring, maintainable choices and explain the trade-offs.