Modern SQL Server Data Warehouse

The Challenge

Organizations often store data across multiple operational systems such as ERP and CRM platforms. These systems generate valuable information but are typically fragmented, inconsistent, and not optimized for analytical workloads. Building reliable analytics requires transforming this raw operational data into a structured, consistent, and query-efficient format that supports business intelligence and reporting. The challenge was to design and implement a complete end-to-end data warehouse architecture capable of ingesting raw data, transforming it, ensuring data quality, and delivering business-ready datasets for analytics.

The Solution

Designed and implemented a modern SQL Server data warehouse using a Medallion architecture to progressively transform raw data into trusted analytical datasets. Developed ETL / ELT pipelines using T-SQL and stored procedures to extract and integrate data from simulated ERP and CRM systems. Implemented multiple transformation stages including data cleaning, standardization, normalization, and enrichment to ensure high-quality datasets ready for analytical consumption. At the final layer, engineered a star schema data mart consisting of fact and dimension tables optimized for business intelligence queries and reporting workloads.