Andrew Muldowney

I am a Backend Engineer focused on data engineering with over 10 years building large-scale distributed systems and real-time streaming pipelines. I design and optimize production data platforms that transform massive volumes of complex data into reliable, high-performing data lakes.

At DoorDash, I architected large-scale Spark and Kafka pipelines, built Iceberg-based data lakehouse storage on S3/EMR, and improved processing runtime and reconciliation accuracy through optimized partitioning and caching. I emphasize observability, lineage, and compliance while enabling real-time financial analytics.

Previously at Square and Uber I led payment data lake modernization and global transaction platforms, implementing stream-to-batch integrations, schema evolution with Iceberg, and automated ETL orchestration to support analytics and ML. I have a track record of reducing costs and latency while increasing data reliability and auditability.

I collaborate closely with cross-functional teams—analytics, finance, compliance, and infrastructure—to define data models, governance, and secure access controls. I mentor engineers, lead technical design discussions, and drive cloud-native, event-driven architectures that scale for enterprise needs.