Michael Ma

I’m a Senior Data Engineer focused on turning complex, multi-source event data into reliable, production-grade datasets for analytics, ranking, and experimentation. I build end-to-end rider lifecycle and marketplace feature pipelines that directly power decision-making at scale.

At Uber, I modeled and operationalized the Ride Session Analytics Platform—covering shopping through matching to trip completion—enabling analytics across 200M+ monthly active users and ~3B+ quarterly trips. I’ve also aligned metric definitions across 10+ teams, improving consistency in marketplace performance reporting.

I’ve engineered scalable lakehouse pipelines (Bronze/Silver/Gold) using Spark, Python, SQL, and AWS S3, processing multi-terabyte daily datasets from thousands of upstream sources. I integrated 5+ heterogeneous data sources into unified analytical tables, while implementing data quality checks for freshness, completeness, duplication, and schema drift.

Previously at Airbnb and DoorDash, I delivered Airflow-based ETL pipelines, batch + near-real-time integrations using Kafka/CDC patterns, and feature datasets supporting A/B testing at hundreds of concurrent experiments. I’m especially energized by optimizing incremental processing for late-arriving data, tuning Spark workloads to reduce runtime, and improving dataset usability through documentation, lineage, and ownership tracking.