Barcelona, Catalonia, Spain
• Saved Amazon SEA EU business from getting shut down for fiscal incompliance by developing an app to create physical IDs for intermodal vehicle routes in compliance with EU law • Developed and maintained batch data pipelines updating datasets queried by 800 monthly users, 2000 data pipelines and 500 000 monthly queries • Built streaming data ingestion pipelines to process real-time package data from Amazon warehouses (150M daily records, 100GB). Increased accuracy of package volume reporting by 5%. Cut delay in forecasting accuracy reporting from 2 weeks to 1 day. • Optimized SQL queries for performance achieving 50% runtime reduction • Created central repository housing my team's internal documentation
• Achieved $15 millions in savings and decreased W.A.P .E. of vehicle type forecasting from 84% to 32% by building AWS data pipelines with Python to automate vehicle‑type allocation. • Automate data warangling tasks with Python • Build ETL data pipelines • Wrote my org's guidelines on Redshift SQL query performance • Build fully automated dashboard
• Clean and verify the integrity of data used in analysis • Produce visualizations, presentations and reports • Design and send out questionnaires Impact: • By adopting R and Python I helped my unit solve several data wrangling issues that couldn't be addressed with Excel