Paulo Sérgio Oliveira Pintor is a software developer with nearly seven years of expertise and currently working at the University of Aveiro - IEETA (Institute of Electronics and Informatics Engineering of Aveiro) as a FCT research fellow in the area of computer science. He holds a Bachelor's degree in Information Technologies and Systems and a master's degree in Information Systems from the University of Aveiro. In what concerns his language skills, he is proficient in English. In addition, Paulo also has great communication skills, critical thinking and multitasking, being also very autonomous, collaborative in teamwork and with a great capacity for leadership and coordination. Throughout his academic path and the investigation and work done for his master's thesis, he developed a deep interest in the field of data engineering with a special focus on Big Data and Spatio-temporal data. He participated in ACM SIGSPATIAL with an article about spatial-data, and he is the third author of an article published in Big Data Research by Elsevier about performance tuning for Big Data systems. He also participated in the research project MoST as a technical developer. He worked as a software developer and database manager in three different companies, and the last one was the University of Aveiro, at sTIC (Serviços de Tecnologia de Informação e Comunicação) in the academic field. In his first were of PhD he was a research fellow in the Augmented Humanity project at the University of Aveiro - DEGEIT (Department of Economics, Management, Industrial Engineering and Tourism). During this time, he also improved his skills with formations in Coaching and Leadership and SCRUM - Project management. Beginning in the second year of his PhD program, he received a research grant from FCT, allowing him to concentrate on the field of data provenance. He developed a versatile application designed to capture data provenance regardless of the database, environment, or data type, all while managing highly complex queries. Currently, his research centers on the interpretation of results, with an emphasis on areas such as summarization and visualization.