The University of Michigan is seeking a data platform to support its data science practice, with the goal of creating a more agile, scalable, and data-driven environment. The selected platform must meet the evolving analytical, governance, and operational needs of the university, while offering robust security, cost efficiency, and integration capabilities. The scope of work includes data engineering, data science, and business intelligence workloads, as well as real-time data processing, machine learning integration, and self-service analytics. The university is looking for a cloud-native platform that can support data ingestion, transformation, sharing, and collaboration across campus units. Some key features and requirements include: • Support for a broad range of data workloads • Robust security and compliance • Scalability and flexibility • Integration with legacy systems and modern cloud-based architectures • Real-time data processing and machine learning integration • Self-service analytics and data governance • Cloud-native platform with support for data ingestion, transformation, and collaboration