Most early-stage data setups are a tangle of cron jobs that breaks quietly. We rebuild them into pipelines that run on a schedule, recover on their own, and tell you when something breaks.
The setup that got you this far is starting to crack, and nobody has time to fix it properly. That's normal at this stage.
A job dies overnight. You hear about it Tuesday, when someone asks why the numbers look off.
Your sharpest engineer keeps getting pulled off product to babysit data jobs.
Two dashboards, two answers. Nobody's sure which one to believe in the meeting.
What worked at ten customers is straining at a hundred, and a rebuild is starting to look unavoidable.
A week or two going through your setup. You get a plain-English list of what's fragile and what to fix first. Fixed price.
We build (or rebuild) the stack: ingestion, a warehouse, transforms, and orchestration that's monitored and safe to re-run. So your team stops second-guessing the numbers.
Keep us on retainer for senior data-infra help when you need it, without hiring a full-timer you're not ready for.
You're seed to Series A, on AWS, your data is growing fast, and you don't have a data engineer yet, so the pipelines get maintained whenever someone has a spare hour. If data is your product, or you're pre-launch with nothing to pipe yet, we're probably not the right fit.
Pine Street brings data-platform engineering from Amazon to startups that can't hire it yet: the same orchestration and reliability work, scaled down. Whoever scopes your project is the one who builds it.
The audit is a quick, fixed-price look at what's breaking and what to do about it. Nothing more than that.
