Data science has become an increasingly popular topic among engineering students and practitioners as high-profile engineering applications of machine learning and artificial intelligence continue to make headlines. Companies in engineering domains are placing a growing emphasis on hiring engineers who can extract insights and create value from the large amounts of data generated, rather than having distinct roles for designers and data scientists working separately. However, the question of who is responsible for teaching these emerging skills remains an outstanding one: Should they be taught in-house by engineering faculty? Should students take generalized courses outside their program, such as those housed in computer science departments? Or should students be responsible for self-teaching outside their formal training? In this commentary, we argue that undergraduate engineering curricula should include contextualized training in programming and data science concepts. We describe our recent eff orts to build a "Data Science Corps" in collaboration with dozens of other institutions throughout the United States and provide specific examples of our recent approach to contextualized data science education in two engineering disciplines, materials engineering and architectural engineering. As engineering programs strive to incorporate new curricula, contextualization becomes vital in bridging the gap between abstract data science concepts and their application in specific engineering disciplines. The collaboration between academia and industry will be crucial for developing effective educational programs that equip future engineers with the necessary skills to thrive in a fast-moving, data-driven world. By embracing this collaboration and integrating data science education within engineering disciplines, we can pave the way for a future where engineers are proficient in both domain-specific knowledge and data science competencies, leading to transformative advancements in the field.