Evaluating new ideas for job scheduling or data transfer algorithms in large-scale grid systems is known to be notoriously challenging. Existing grid simulators expect to receive a realistic workload as an input. Such input is difficult to provide in absence of an in-depth study of representative grid workloads. In this work, we analyze the ATLAS workload processed on the resources of NDG Facility. ATLAS is one of the biggest grid technology users, with extreme demands for CPU power and bandwidth. The analysis is based on the data sample with ~1.6 million jobs, 1,723 TB of data transfer, and 873 years of processor time. Our additional contributions are (a) scalable workload models that can be used to generate a synthetic workload for a given number of jobs, (b) an open-source workload generator software integrated with existing grid simulators, and (c) suggestions for grid system designers based on the insights of data analysis.