Data Engineering

Retail Analytics Data Warehouse

A comprehensive data warehouse solution for retail analytics featuring dimensional modeling, ETL processes using SSIS, and analytical capabilities for sales and returns. The warehouse implements a star schema with 6 dimensions and 2 fact tables, processing millions of transactions with built-in validation, error handling, and auditing.

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Gallery

Screenshots & diagrams

Architecture

How it's built

Source Database (SQL Server)SSIS ETL Packages (Extract, Transform, Load)Star Schema DW (6 Dimensions + 2 Facts)Power BI Dashboard (4 pages)

Data engineer — designed dimensional model (Kimball methodology), built SSIS packages, created Power BI dashboard.

Highlights

Key achievements

01Star schema with 6 dimension tables (Date, Customer, Product, Supplier, PaymentMethod, Campaign) and 2 fact tables (Sales, Returns)
02SCD Type 2 slowly changing dimensions for Customer and Product — full history tracking
03SSIS ETL pipeline with data type conversions, dimension key lookups, validation, and error handling
04$57.34M total revenue analyzed across 54K orders and 126K quantity sold
054-page Power BI dashboard: Executive Overview, Sales Deep Dive, Customer Analysis, Returns Profitability

Stack

Technologies used

SQLPython

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