Enterprise SQL Pipelines — Making Data Trustworthy
Architected 200+ modular SQL models across Snowflake and Databricks for multiple enterprise clients. The goal was never the models themselves — it was giving leadership a data layer they could trust. Every metric defined once, tested, documented, and traceable.
Outcomes
200+ SQL models in production
40% fewer data quality incidents
Every metric defined once and traceable
The Problem
Multiple enterprise clients had data built ad-hoc: inconsistent naming, no tests, no documentation, and no way to know if something broke until a C-suite dashboard showed the wrong number. The real problem wasn't the SQL — it was that nobody trusted the output. When leadership questions a number, someone spends two days investigating. Eventually the dashboard just stops getting used.
The Approach
Audited all existing SQL and categorised by source, staging, intermediate, and mart layers using the medallion architecture.
Rebuilt every model with consistent naming conventions and documentation standards. One place to define each metric — not five slightly different versions across different reports.
Added automated data quality tests (not-null, unique, referential integrity) for every critical model, with severity thresholds so the right people get alerted for the right failures.
Established peer review and version control processes across all client environments so data quality issues are caught before they reach production dashboards.
The Result
Data quality incidents down 40% over six quarters. More importantly: when a CFO asks why two reports show different revenue, there is now a clear answer traceable through the pipeline to the exact logic. That is what builds trust. And trust is what makes dashboards get used.
Full Stack
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