Expert-finding systems aim to identify knowledgeable individuals in specific domains based on evidence such as publications, activities, and social network data, with academic uses including allowing identification of potential supervisors, collaborators, or peer reviewers. However, most existing benchmark datasets for academic expert finding contain only publication information and lack authentic query logs. We introduce the Find an Expert (FaE) dataset from the University of Melbourne, comprising three interconnected components: structured profiles for 8, 984 academic staff providing text biographies and research interests, and lists of (and links to) publications and current projects; 712, 937 interaction records captured over 239 days in 2025, that record queries, clicks, and temporal patterns; and system-generated rankings for 530 queries where users clicked on profiles. Together, these resources provide the first publicly available expert-finding dataset combining profile data, log interactions, and system outputs, thereby allowing deep investigation of expert search systems.