Reynold Xin

Reynold Xin is a co-founder and the chief architect of Databricks, and one of the principal engineers behind Apache Spark, the open-source distributed data-processing engine that underpins much of the modern big-data and AI stack. He holds a Ph.D. in computer science from the University of California, Berkeley, where he studied under Ion Stoica and Michael J. Franklin at the AMPLab, and a BA.Sc. from the University of Toronto.

As a doctoral researcher, Xin became one of the most prolific contributors to the Apache Spark project, taking a lead role in designing and building GraphX, Spark's graph-processing library, and driving 'Project Tungsten', a major initiative to rework Spark's execution engine for far greater memory and CPU efficiency. He also co-designed Spark's DataFrames API and served as release manager for the landmark Spark 2.0 release, work that helped Spark supplant Hadoop MapReduce as the default engine for large-scale data processing.

In 2013, Xin joined Matei Zaharia, Ion Stoica, Ali Ghodsi and other Spark creators in co-founding Databricks to commercialise the technology. As chief architect, he has remained close to the technical core of the company through its transformation from a Spark-hosting service into a full 'lakehouse' platform for data engineering, analytics and machine learning, and more recently into a key infrastructure provider for enterprise generative AI.

Xin's engineering credentials extend beyond Spark: in 2014 he led the Databricks team that set a world record in the Daytona GraySort benchmark, sorting 100 terabytes of data faster than any system before it, including Hadoop-based clusters many times larger. He has been repeatedly recognised as one of the most-cited researchers in the database systems field, reflecting both his academic output and the industry-wide adoption of the systems he helped build.

As Databricks has grown into one of the most valuable private technology companies, with valuations reported above $100 billion through 2025 and 2026, Xin has continued to speak publicly at conferences such as the Data + AI Summit and VLDB about the evolution of the lakehouse architecture, streaming data systems, and the company's Lakebase database product, cementing his role as one of the clearest technical voices in enterprise data infrastructure.