Real work. Real data.

Built an end-to-end ETL pipeline using Python and SQL to consolidate raw marketing spend, impression, and revenue data into a single Power BI model. The final dashboard gave stakeholders a clear view of which campaigns were generating profitable returns and which were burning budget without conversion.

Used Python, SQL, and Power BI to analyse Netflix's global content library and streaming performance data. Automated an ingestion pipeline to surface content survival rates, genre performance by region, and production investment patterns — giving a data-backed framework for programming and acquisition decisions.

Applied BI tools to transform raw point-of-sale transaction data into a strategic roadmap for a retail café operator. The analysis identified peak trading windows, underperforming SKUs, and inventory inefficiencies — enabling data-backed decisions on staffing, stock management, and menu pricing.

Built a single-page executive Power BI dashboard on a 100,000-customer e-commerce dataset covering $9.32M in gross revenue across 6 countries and 5 acquisition channels. The dashboard surfaces channel mix distribution, geographic revenue concentration, average order value, and a full-year monthly revenue trend — revealing a Q4 seasonal dependency pattern that signals an off-season demand generation problem for any real e-commerce operator.