Tembinkosi Vikani Dube
Data Engineer building scalable ETL pipelines & insights
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This project analyses transactional supermarket sales data to uncover insights into sales performance, customer behaviour, and product profitability. The objective is to transform raw retail data into decision-ready insights using a full analytics workflow covering data ingestion, cleaning, analysis, and visualization.
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This project demonstrates retail sales analytics using SQL, Python, and Power BI. It includes data cleaning, transformation, analysis, and visualization of sales, customer, product, and inventory data to generate actionable business insights.
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This project analyzes user behavior across a large-scale e-commerce platform to identify conversion bottlenecks, revenue drivers, and growth opportunities.
Using a combination of Python, SQL, and Power BI, the analysis transforms raw event-level data into actionable business insights aligned with real-world analytics practices used by companies like Amazon and Shopify.
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This project is an end-to-end data engineering ETL pipeline that ingests retail datasets from Kaggle, validates and cleans them, performs feature engineering, and loads structured data into a SQL Server database. It also generates a data quality observability layer for tracking dataset health across runs.
The pipeline is designed to simulate real-world production workflows used in retail analytics and data engineering systems.
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