Ensuring high software quality in development cycles with frequent updates is critical, especially in Agile and CI/CD environments. Just-In-Time Software Defect Prediction (JIT-SDP) has emerged as a promising solution for finding bugs early, as it enables immediate identification of changes prone to defects. JIT-SPD models based on Machine Learning focus primarily on project- and change-specific features, such as number of lines added and number of files modified in the change. Recent research has started to investigate developer-related features for defect prediction. However, these studies overlook information about developers’ work habits and cross-project activities. In this paper, we try to fill this gap by introducing a set of developer-centric features for JIT-SDP, which span through temporal aspects (when do developers usually make commits?), change-related aspects (how do developers usually make commits?), and project-related aspects (how are the contributions distributed among different repositories?). We conducted an empirical evaluation to understand if such features allow to improve ML-based JIT-SPD models and evaluated the importance of developer-centric features on the performance of the model. Our results show that integrating developer-centric features improves model performance. We observed a +15.48% precision and +10.47% recall in a within-project evaluation and +14.59% precision and even +85.83% recall in cross-project evaluation.