> I created this organized Product Inventory Tracker for a small business client. > What I did: > - Entered 500+ products with Product ID, Product Name, Category, SKU, Quantity, and Pricing > - Organized data into clean columns: Product, Category, SKU, Stock Level > - Added formulas for Total Value and Low Stock Alerts > - Used color-coding and filters for easy searching > - Checked for duplicates and errors - 100% accuracy > Tools Used: Google Sheets, Microsoft Excel, Google Drive > Delivery: Same day, editable sheet + PDF export > Result: Client can now track stock, avoid running out of products, and see total inventory value in one click. > I can do the same for you — Data Entry, Inventory, Customer Lists, PDF to Excel. Fast, accurate, and organized!

Built a complete SQL Server (T-SQL) data warehouse for a fictional multi-channel, multi-currency retailer (synthetic dataset created for demonstration purposes). Delivered: staging layer loaded from 17 CSV files (~397K rows); one transactional, re-runnable ETL stored procedure that cleans duplicates, mixed date formats and inconsistent labels; a star schema with keys and constraints; reporting views with business rules defined once (net revenue in USD, contribution margin); automated data-quality tests; and 17 analytical queries (cohort retention, RFM segmentation, Pareto/ABC, constant-currency growth, carrier failure alert, inventory health, CAC vs platform claims, budget vs actual). Key insight: marketplace shows 51% gross margin but only 24.5% contribution margin after commission, shipping and refunds.

For the mixed-date cleanup, I'd include a case like 03/04/2026 in the quality tests: it parses under two conventions but can land in different reporting periods. An invalid-date check alone won't catch that. A source-specific date rule, with ambiguous rows held for review and...