User surveys are essential to user-centered research in many fields, including human-computer interaction (HCI). Survey personalization—specifically, adapting questionnaires to the respondents’ profiles and experiences—can improve reliability and quality of responses. However, popular survey platforms lack usable mechanisms for seamlessly importing participants’ data from other systems. This paper explores the design of a data-driven survey system to fill this gap. First, we conducted formative research, including a literature review and a survey of researchers (𝑁 = 52), to understand researchers’ practices, experiences, needs, and interests in a data-driven survey system. We designed and implemented a minimum viable product called Data-Driven Surveys (DDS), which enables including respondents’ data from online service accounts (Fitbit, Instagram, Spotify, GitHub, etc.) in survey questions, answers, and flow/logic. Our system is free, open source, and extensible. It can enhance the survey research experience for both researchers and respondents.