In modern computing education, constructing fair and data-driven mechanisms to assess teamwork and collaboration skills in student teams remains a persistent pedagogical challenge. Instructors struggle to balance the subjective nature of peer evaluations with the need for actionable feedback that aligns with professional dispositions. Existing CS education research provides useful insights through qualitative surveys and peer evaluations. However, quantitative frameworks can help capture dynamic team interactions and deliver timely, calibrated feedback on collaboration. We present a data-driven, iterative intervention workflow that uses quantitative metrics to identify group-level weaknesses and guide simple, weekly course team-led meetings targeting effective communication, and team cohesion. Sequential Peer Assessment (SPA) was conducted through an open-source platform TEAMMATES at the semester midpoint to identify key areas that needed work and again six weeks later to measure shifts in defined team performance metrics. Our analysis revealed a 22% increment in average peer ratings of the class, along with improvement in team cohesion and perception gaps among the students. This implied that more students were now working collaboratively in their teams. This repeatable, scalable process aligns directly with ABET student outcomes and competency-based education goals, improving professional dispositions and promoting equitable teamwork in computing courses.