Slash data-prep time by 80% with Power Query, Python & automated pipelines for B2B firms.
Turn Messy Data Into a Clean Central Repository
Problem
Analysts spend 60–80% of time prepping data; errors slip into reports.
Solution
I create ETL workflows using Power Query for Microsoft stacks and Python/Pandas for other sources. Data is deduped, validated, and landed in a single model table automatically.