The need for high-quality, large-scale, goal-oriented dialogue datasets continues to grow as virtual assistants become increasingly widespread. However, existing publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity. We introduce the MultiDoGO dataset to overcome these limitations. With a total of over 65,000 dialogues across seven domains, MultiDoGO is a magnitude larger than MultiWOZ, the current largest comparable dialogue dataset. We employ a Wizard-of-Oz approach wherein a crowd-sourced worker (the “customer”) is paired with a trained annotator (the “agent”). Including a trained participant in each conversation allows us to increase linguistic diversity by avoiding templates, while maintaining conversational coherency. We provide intent class annotations unique to customers and agents, along with applicable slot labels at the conversation turn level. We present our strategies for eliciting and annotating a dialogue dataset in a manner that scales across languages and modalities. We establish strong neural baselines for intent classification and slot labeling tasks on each domain.