At Plaid, I build batch and streaming pipelines for Transactions, Link, Auth, and Signal using Databricks, Spark, Kafka, Delta Lake, Snowflake, Python, and SQL. My work reduced transaction data latency by 30%, improved account verification processing by 28%, and helped achieve 99.8% pipeline reliability through a centralized AWS lakehouse.
Previously at Apple, I built analytics pipelines for the App Store, Apple Music, and Apple Services, reducing end-to-end data latency from five hours to under one hour. I also developed ETL frameworks, real-time event pipelines, and Snowflake data marts that improved processing efficiency, reporting latency, and analytical query performance.
I began building ETL, streaming, data modeling, and quality frameworks at MyFitnessPal and Akamai, supporting nutrition, user activity, CDN traffic, and network-performance analytics. I enjoy partnering with product, ML, analytics, and backend teams to deliver scalable data products.
