Choices in persistent data models and data organization have significant performance ramifica- tions for data-intensive scientific computing. In experimental high energy physics, organizing file-based event data for efficient per-attribute retrieval may improve the I/O performance of some physics analyses but hamper the performance of processing that requires full-event access. In-file data organization tuned for serial access by a single process may be less suitable for opportunis- tic sub-file-based processing on distributed computing resources. Unique I/O characteristics of high-performance computing platforms pose additional challenges. The ATLAS experiment at the Large Hadron Collider employs a flexible I/O framework and a suite of tools and techniques for persistent data organization to support an increasingly heterogeneous array of data access and processing models.