Lean Data Engineering
We structure, clean, and pipeline your business data to ensure it is ready to power custom machine learning models and AI tools.
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What This Covers
AI is only as good as the data it runs on. We build the lean data pipelines that take your business data — wherever it lives — and make it clean, structured, and accessible for AI and analytics. Designed for the scale of a mid-market business: reliable, maintainable, and without the over-engineering of an enterprise data platform.
Included in scope
- Data source audit and mapping
- ELT pipeline design and implementation
- Data warehouse setup (Postgres, BigQuery, Snowflake)
- dbt transformation and data modelling
- Data quality monitoring and alerting
- AI-ready data preparation (feature engineering, vector indexing)
When to Engage
Situations We're Built For
"Our AI features need fresh data but we don't have pipelines."
We build the pipelines that keep your AI features current — from source to production, with monitoring so you know when something breaks.
"We're running our business out of spreadsheets and CSVs."
We design and build a proper data layer — warehouse, pipelines, dashboards — that scales with your business without requiring a data team.
"We need to prepare training data for a custom model."
We audit your raw data, design the cleaning and feature engineering pipeline, and deliver a dataset ready for model training.
How We Work
Our Approach
Data audit
Where is your data? What format? What quality? What does the business need from it?
Architecture design
Warehouse selection, ingestion patterns, and transformation design for your scale.
Pipeline build
Reliable pipelines with error handling, retry logic, and alerting. Not notebooks.
Transformation layer
dbt models that produce clean, business-logic tables your AI and analytics can use.
Observability
Data quality checks, pipeline monitoring, and alerting so you know before the business does.
Technologies
Industries
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