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Every source, one warehouse, nightly by 6 a.m.
Challenge
Operations data lived in an ERP, an e-commerce platform, and a pile of spreadsheets. Every question meant re-exporting and reconciling by hand, and no two people arrived at the same total.
Approach
We built an orchestrated ELT pipeline that lands all sources in a modeled warehouse. Incremental loads keep the run inside its window as volume grows, tested transformations keep the model honest, and freshness checks confirm the data is complete before the morning shift starts. Downstream dashboards read one reconciled source of truth instead of a fresh export each time.
Architecture
ERP + e-commerce + spreadsheets → Airflow ELT → dbt models → freshness & quality tests → warehouse → dashboards
Airflow · dbt · BigQuery — incremental loads, tested models, freshness SLAs
Results
- One reconciled warehouse, landed nightly before the 6 a.m. shift
- Incremental loads keep the pipeline inside its window as data grows
- Freshness and quality tests block bad data from reaching dashboards
* Illustrative figure from a representative engagement, shown to convey typical scope. Replaced with client-verified numbers before publication.