Predictive Maintenance (PM) is crucial for optimizing economic resources, operational efficiency and fundamental infrastructure systems. Its primary function is to foresee and prevent critical infrastructure breakdowns or failures through digital approaches. As industries increasingly rely on data-driven approaches, the need for a skilled workforce proficient in predictive maintenance systems becomes paramount. Recognizing the transformative impact of emerging technologies on the transportation landscape, this paper envisions a future employee profile characterized by a multidisciplinary skill set, encompassing data analytics and domain-specific expertise. This study proposes six matrices covering the competence areas as defined by key actors in the transportation sector during multiple online workshops. These matrices could operate as an educational benchmark to identify the potential challenges and opportunities within the transportation workforce industry. They could also equip transportation sector employees with the requisite skills contributing to more resilient and efficient transportation infrastructures. Finally, a comprehensive training toolkit is proposed i) to include multiple modules (each for a specific competence area) and ii) to provide a structured course that integrates theoretical foundations with practical applications.