Situation
A financial services organization was consolidating operational data originating in more than 100 SQL Server databases into Snowflake.
Moving the data was only part of the challenge. Operational database structures had been designed to run applications—not to support enterprise reporting, longitudinal analysis, or data science.
The organization needed a dependable analytical layer that could interpret change-data-capture history, reconcile changing records, improve reporting performance, and turn the resulting warehouse into a usable foundation for analytics and modelling.
GTP’s work
- Designed extensive SQL and Snowflake transformation logic to convert operational source structures into consistent, reporting- and analysis-ready datasets.
- Developed query and data-derivation logic for interpreting CDC records, including reconstruction of reliable current and point-in-time views while preserving the historical information needed for more accurate reporting.
- Performed substantial data exploration, query design, performance optimization, and analytical restructuring to make the warehouse more suitable for internal reporting and subsequent data-science workloads.
- Used agentic programming to accelerate portions of development, analysis, and test creation, while subjecting the resulting data and reporting logic to extensive automated and agentic testing.
Result
The transformed Snowflake environment successfully supported internal reporting with improved query performance and a more accurate representation of information through time.
More importantly, the work established an analytical layer between operational systems and downstream consumers: raw application data became structured information that could support reporting, historical analysis, quantitative modelling, and future AI use cases.
The result was not simply a database migration. It was the conversion of operational data into a reusable financial-data foundation.
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