Andreas Kretz is a Data Engineer and self-described plumber of data science. While data scientists get the headlines, Andreas focuses on the plumbing that makes their work possible: the platform architecture, tools, and techniques used to build modern data science platforms that actually run in production.

He is best known for making data engineering approachable. Through his writing, talks, and his project Team Data Science, he explains how the pieces of a data platform fit together, from ingesting and processing data to building pipelines and deploying machine learning models. His content covers practical technologies such as AWS and Kafka, and topics like moving ML work out of notebooks and into streaming, production-grade systems.

For engineers who want to understand data platform architecture without wading through vendor documentation, Andreas is one of the clearest guides in the field, combining hands-on data engineering experience with a talent for visualizing and simplifying complex systems.