Warehouse modelling
Staging, intermediate and mart layers with naming your analysts can follow without a guide.
Service
Data engineering
Warehouse migration, ELT pipelines, dbt modelling and streaming, built to be read and changed by your own engineers.
The problem
Reporting is slow and nobody can say why. Pipelines break quietly and get discovered by a business user. The same transformation exists in four places, each slightly different.
Underneath almost all of it is the same cause: the data layer grew one urgent request at a time, and nobody was ever given time to design it.
How we approach it
We start with what already works. Replacing a warehouse is occasionally the right answer, but it is rarely the first one and never the cheapest way to prove an idea pays for itself.
The first working pipeline usually lands inside three weeks. From there the work is reviewed in your sprint, by your engineers, against software that runs.
Standards we bring
Staging, intermediate and mart layers with naming your analysts can follow without a guide. Tests on freshness, uniqueness and referential integrity, running on schedule. Lineage generated from the project rather than drawn by hand.
What you own at the end
A repository, documented, with CI configured and runbooks written for someone who did not build it. Nothing to transfer, because none of it was ever ours.
What we do
The activities this covers
ELT pipelines
Ingestion from your sources with retries, backfills and alerting that names the failing table.
dbt standards
Project structure, macros, tests and CI so changes are reviewed like any other code.
Streaming
Near-real-time paths where they pay for themselves, batch everywhere else.
Data quality
Freshness, uniqueness and referential tests wired into the schedule, not run by hand.
Documentation
Lineage and column descriptions generated from the project, current by default.
What you get
Deliverables, not slideware
- A repository you ownTransformations, infrastructure and CI configuration, commented and reviewed.
- Test suiteTests running on schedule, with alerts routed to a channel your team reads.
- Cost baselineWhat the work costs to run per team, with a monthly budget alert.
- Architecture diagramCurrent state and target state, kept up to date through the engagement.
- RunbooksWhat to do when something fails at 3am, written for someone who did not build it.
- Two weeks of trainingHands-on sessions with the analysts and engineers who take it over.
Related work
Where this service led the engagement
Ridgeline Bank · Financial services
A month-end close that took eleven days
Finance rebuilt the same figures by hand every cycle. We modelled the ledger in the warehouse, defined forty metrics once, and automated the reconciliation.
11 → 3 days to close
Hollis Logistics · Logistics
A warehouse bill that doubled in a year
Nobody could see which queries cost what. We added cost attribution per team, rewrote the twelve worst models, and set a monthly budget alert.
-46% platform cost
Questions
Questions about data engineering
Still unsure? Call (216) 123 45678 and ask for whoever is on the desk.
Ask a questionNo. Most engagements improve what you already run. We only recommend migration when the current platform cannot meet a requirement you have already committed to.
You do. Everything is written in your repository, under your cloud account, on open tooling. There is nothing to transfer at the end because it was never ours.
That is the normal arrangement. Your engineers review our pull requests and we review theirs. Teams that stay out of the work rarely take it over successfully.
First working increment usually lands inside three weeks. We demo weekly against running software, not plans.
Tell us which number you cannot trust
Send one paragraph about the report that always starts an argument. A data lead, not a salesperson, replies within one business day.