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Data Engineering
Model quality is capped by data quality. Before any modelling, we make sure the data is collected, cleaned, versioned and delivered reliably. Good data engineering is unglamorous, and it's usually where AI projects are won or lost.
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What we deliver
- Batch and streaming pipelines from your sources to a warehouse or lake
- Data quality checks and alerting when something breaks upstream
- Dataset versioning and lineage, so results are reproducible
- Labelling workflows and review tooling
- Schema design for analytics and ML use
- Cost-aware storage and compute
Where it's used
Consolidating data from many systems
Preparing training data for new models
Fixing flaky, undocumented pipelines
Building a foundation for analytics and AI
How we work
- Discover. We look at your data, define success metrics and agree what "good" means before building anything.
- Build. We start with a baseline, iterate with measured evaluation, and keep you in the loop with working demos.
- Ship & monitor. We deploy with tests and rollback plans, then monitor quality, cost and drift.
Tools we use
PostgresBigQuerySnowflakeSparkKafkaAirflowdbtGreat Expectations
Questions
Do we need a data warehouse first?
Not always. We start with the question you want answered and build the lightest foundation that supports it.
Can you clean up our existing pipelines?
Yes. We often start by auditing what exists, adding tests and monitoring, and then simplifying.