Despite substantial instructional attention to large-enrollment university courses in science, technology, engineering, and mathematics (STEM), they tend to have high rates of D, F, and Withdraw (DFW) at the introductory level that disproportionally disadvantage historically minoritized and underrepresented students, such as students identified as women, low-income, first-generation, or of color. While postsecondary institutions have recently explored big data and learning analytics to drive their institutional student success efforts, well-known shortfalls in student success in large STEM courses remain. This chapter documents an evidence-based approach at a large, R1 midwestern university that enriches robust data infrastructure with qualitative ethnographic methods. Applied to a gateway computer science course, these methods center students’ day-to-day learning realities, including disparate educational opportunities, in ways that interrogate barriers to and shortfalls in student success. The resulting case study describes our ethnographic approach, the shortfalls it uncovers, our future directions with this work, and how other faculty members and institutions can apply lessons learned to promote efficacy, attainment, and equity in gateway STEM courses. Implementations drawn from course vignettes point to revisions in design and preparation of group learning activities, strategic integration of lecture and lab sessions, course navigation aids, and pedagogical training for teaching assistants.