Dan Wrenn is a highly experienced Principal Solutions Architect with a strong background in data engineering, analytics, cloud architecture, and machine learning. He has a proven track record of helping organizations leverage data to drive business outcomes, particularly within the Databricks ecosystem and previously with technologies like Apache Spark, Hadoop, and various cloud platforms (AWS, Azure, GCP). Dan is adept at designing and implementing scalable data solutions, leading technical teams, and communicating complex technical concepts to both technical and non-technical audiences. He is passionate about enabling data-driven transformations and fostering innovation through modern data platforms.
Dan Wrenn's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Leads technical strategy and solution design for key enterprise customers, helping them adopt and optimize the Databricks Lakehouse Platform for advanced analytics, AI, and data engineering workloads.
Co-authored a widely recognized O'Reilly Media book on Apache Spark, contributing significantly to the understanding and adoption of distributed data processing.
Frequently presents at industry conferences (e.g., Data + AI Summit, Spark Summit) and contributes to the data community through talks, articles, and knowledge sharing.
Demonstrated deep expertise across a range of big data technologies including Apache Spark, Delta Lake, MLflow, and cloud-native data services, enabling complex data-driven projects.
Successfully architected and guided the implementation of numerous large-scale data platforms for Fortune 500 companies, resulting in improved data accessibility, performance, and analytical capabilities.
Florida State University
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