Fintech Data Warehouse

A rapidly growing fintech startup was struggling with 'data sprawl'. Customer information was fragmented across CRM, transaction logs, and support tickets, making it impossible to calculate accurate Customer Lifetime Value (CLV) or identify churn patterns.

Their legacy PostgreSQL database was struggling with complex analytical queries, leading to slow report performance and frustrating the marketing and product teams. The lack of a scalable architecture was becoming a blocker for their Series-B funding round.

I designed and implemented a modern star-schema Data Warehouse on Azure Synapse. I used dbt (Data Build Tool) for modular SQL transformations and built robust ETL pipelines using Python to ingest real-time customer behavioral data.

The architecture focuses on scalability and cost-efficiency, utilizing partitioned storage and optimized indexing. I also established a data governance framework to ensure data quality and lineage throughout the warehouse.

I integrated 'Data Health Checks' into the CI/CD pipeline, ensuring that any breaking changes in upstream systems are caught before they impact the truth-layer of the warehouse.