We introduce DialNav, a novel dialog-based navigation task, where an embodied agent (Navigator) collaborates with a remote guide (Guide) through multi-turn dialog to reach a goal location. Unlike prior works our setting requires Guide to infer Navigator's location based on dialog, making dialog crucial for success. To support this task, we collect and release Remote Assistance in Navigation (RAIN) dataset, human-human dialog paired with navigation trajectories in photorealistic environments. We design a comprehensive benchmark, evaluating navigation and dialog, and conduct extensive experiments analyzing the impact of different Navigator and Guide models. We highlight key challenges and publicly release the dataset, code, and evaluation framework to foster advancements in dialog-based embodied AI.