The functional interaction structure of a team captures the preferences with which members of di erent roles interact. This paper presents a data-driven approach to detect the functional interaction structure for software development teams from traces team members leave on development platforms during their daily work. Our approach considers di erences in the activity levels of team members and uses a block-constrained con guration model to compute interaction preferences between members of di erent roles. We apply our approach in a case study to extract the functional interaction structure of a product team at the German IT security company genua GmbH . We subsequently validate the accuracy of the detected interaction structure in interviews with ve team members. Finally, we show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios. Speci cally, we evaluate the level of knowledge di usion in the team and identify areas where the team can further improve. Our approach is computationally e cient and can be applied in real time to manage a team's interaction structure.