Marcell Tajdi
About Marcell Tajdi
Marcell Tajdi is a Data Engineer with a background in various roles within the field of data engineering and analytics. He has worked for notable companies such as IBM, Lynx Analytics, and ZEISS Digital Innovation, and holds degrees in Computer Science Engineering and Business Informatics from Budapest University of Technology and Economics.
Work at ZEISS Digital Innovation
Marcell Tajdi has been employed at ZEISS Digital Innovation as a Data Engineer since 2023. In this role, he contributes to data engineering projects and initiatives within the organization. His position involves utilizing his technical skills to enhance data processes and support the company's digital transformation efforts.
Previous Experience in Data Engineering
Prior to his current role, Marcell Tajdi worked at IBM as a Data Engineer and ETL Developer from 2020 to 2022. He was based in Budapest, Hungary, where he focused on data extraction, transformation, and loading processes. Before IBM, he spent 10 months at Lynx Analytics in 2019 as a Data Engineer, further developing his expertise in data management.
Early Career in Engineering
Marcell Tajdi began his career in engineering at Telekom HU as a Junior Engineer from 2015 to 2016. He later worked at IP Systems Ltd. as a Junior Tester for 5 months in 2016. His early experiences laid the groundwork for his technical skills and understanding of engineering processes.
Education and Expertise
Marcell Tajdi studied at Budapest University of Technology and Economics, where he earned a Bachelor of Science in Computer Science Engineering from 2011 to 2017. He further pursued a Master of Science in Business Informatics, specializing in Analytical Business Intelligence, from 2017 to 2019. His educational background provides him with a comprehensive understanding of IT business processes.
Technical Skills and Specialization
Marcell Tajdi possesses a diverse skill set that includes programming languages such as Python, SQL, Java, JavaScript, and C++. His technical knowledge is complemented by his academic focus on man-machine learning, which he explored in his final thesis aimed at improving classification processes.