• Challenge: An aging on-premise ETL architecture created high maintenance costs, data silos, and performance bottlenecks, with daily processes handling 500M+ records and delaying financial insight and revenue recovery.
• Action: Led a large-scale migration to Azure Data Lake and Databricks, establishing a unified single source of truth by integrating disparate enterprise systems. Re-architected ETL pipelines to enable parallel processing, optimized data flows around high-value transactional records, and eliminated manual bottlenecks through Robotic Process Automation (RPA).
• Result: Reduced daily data processing volumes by 80% (500M → 100M records), significantly improving performance, reliability, and time-to-insight. Enabled recovery of millions in outstanding accounts receivable, ensured regulatory compliance, and lowered operational costs and risk through a simplified, scalable cloud architecture.
Location
Orange, United States
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