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A powerful data workflow requires a balance of system logic and behavioral intuition. My core metrics are currently tracking at a 98% efficiency for human/behavioral data pattern analysis and a 96% rating for high-stress execution, alongside an 82% standard on technical system logic puzzles. This puts my strategic intuition in the top elite bracket for managing and cleansing raw datasets. Actively open for clean data formatting and market analytics projects."
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Creatives on Contra have earned over $150M and we are just getting started
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How should performance data be presented to busy leaders: as a simple signal or as deeper context they can explore?
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ABC Classification (Pareto Analysis) — SQL-Based Inventory Prioritization
Analyzed 293,569 orders and 2,350 SKUs to identify which products actually drive revenue — and which don't.
Approach:
Built a CTE-based ABC classification in SQL Server, ranking products by cumulative revenue contribution using window functions (SUM() OVER, CUME_DIST(), PERCENT_RANK()), then segmented into three tiers based on Pareto thresholds.
Result:
Category A (20% of products) → 70%+ of total revenue
Category B (30% of products) → 20-25% of revenue
Category C (50% of products) → 5-10% of revenue
Business impact:
This reprioritizes where operational effort should go — tighter stock monitoring and faster reordering for Category A, reduced tracking overhead for Category C. The same logic fed into a related late-delivery root cause analysis on the same dataset.
Stack: SQL Server · Tableau
I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.
Synthetic demo results:
• 20 raw order records
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• 2 duplicates removed
• 2 invalid rows quarantined
• $3,690 paid revenue reconciled
The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.
The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.
For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?
