At Cardinal Health, I architected a Google Cloud Platform, Databricks, and Snowflake data platform that brings together healthcare supply-chain information from 14+ enterprise source systems. Its batch and near-real-time pipelines process approximately 18M records per day and reduced end-to-end data latency by 37%.
I implemented a Medallion Lakehouse and developed reusable ETL/ELT pipelines and dimensional models for inventory, fulfillment, shipment, supplier performance, and demand analytics. I also established data quality, observability, governance, and metadata practices for trusted, AI-ready datasets.
At Atlassian, I led the design and development of an enterprise analytics platform processing over 20TB of product, customer, and operational data daily across Jira, Confluence, Bitbucket, and Trello. I built batch and streaming pipelines that brought data availability latency from 24 hours to under 30 minutes.
Earlier, as a Data Engineer at Deloitte Consulting, I delivered data warehousing, big data, and analytics solutions for clients across several industries. I’ve also mentored junior engineers and prepared governed datasets for AI/ML use cases, including demand forecasting and inventory anomaly detection.
