05 / Data Engineering
Data platforms & pipelines
Ingestion, transformation, and storage that makes the models above possible — pragmatic data platforms sized to the problem, not a lakehouse you don't need yet.
Who this is for
Teams where the model isn't the blocker — the data is. Sources that don't talk to each other, a warehouse that's really a folder of spreadsheets, or a pipeline that breaks every time an upstream schema changes.
The problem
Most AI projects stall on data, not modelling. We size the platform to the actual problem: a well-built pipeline into a right-sized warehouse, not a lakehouse architecture built for a scale you don't have.
What we deliver
- Ingestion pipelines from your actual sources — APIs, databases, files, event streams
- A transformation layer with tests, so a schema change fails loudly instead of silently
- A warehouse or lake sized to your current and near-term scale
- Documentation a future engineer — ours or yours — can actually follow
In practice
4 sources → 1
disconnected spreadsheets and exports consolidated into one tested pipeline
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