Pipelines that run without anyone watching them
Automated ingestion, transformation and delivery, with tests and alerting built in. If something breaks at 3am you find out before your CFO does.
Any of these sound familiar?
"Someone exports a CSV every Monday"
Reporting depends on a person remembering a manual step, and it stops the week they're on leave.
"The numbers don't match"
Two teams pull what should be the same metric from different places and get different answers.
"We found out the data was stale last week"
Failures are silent. They surface days later, when someone notices a chart looks wrong.
"Adding a new source takes a month"
Every integration is bespoke, so nothing built for the last one gets reused for the next.
The full pipeline, not just the extract
Source ingestion
Connectors for your databases, SaaS tools and APIs, with incremental loads and schema-change handling.
Orchestration
Dependency-aware scheduling with retries, backfills and a clear owner for every failure.
Transformation layer
Modelled in dbt under version control, so every metric has one definition and a documented lineage.
Data quality tests
Freshness, row count, uniqueness and referential checks that fail the run rather than pass bad data downstream.
Observability and alerting
Pipeline health surfaced where your team already works, with alerts that name the model that broke.
CI/CD for data
Changes reviewed and tested before they reach production, the same way application code is.
Tools we reach for
We work with what you already run where that makes sense. Where it doesn't, these are our defaults.
A typical engagement
- Weeks 1–2
Audit and design
Source inventory, volume and freshness requirements, target architecture agreed in writing.
- Weeks 3–6
Build
Pipelines built source by source. Each one live and tested before the next begins.
- Weeks 7–8
Harden and hand over
Alerting, runbooks, documentation and training so your team owns it after we leave.