All success stories
Data platforms/Data engineering/SC-09
ETL / ELT & Data Quality Pipeline
What this system is for
A reproducible analytics-engineering pipeline with SQL transformations and explicit quality gates.
Reference economics
40 h/month reconciliation → 50% less rework → 20 h/month released.
Reference arithmetic, not a measured client result.
40 h
Reference scenario
50%
Illustrative improvement
20 h
Decision value
The success story in 30 seconds
Business problem
The issue is reliability: inconsistent contracts, late failures and unclear ownership turn analytics into manual reconciliation.
What changed
Define source and schema contracts.
Business utility
The value is less reconciliation, fewer silent failures and a faster path from operational events to trusted decisions.
Decision
Data engineering
System
ETL / ELT & Data Quality Pipeline
Validation
Sources · Quality tests
Core stack
dbt · DuckDB · Python