The global travel and tourism industry experienced a 4% rise in new job postings related to cybersecurity in Q3 2022 compared with the previous quarter, according to GlobalData’s whitepaper on Cybersecurity in Travel & Tourism – Hiring Activity in Q3 2022. This compares to a 25% increase versus Q3 2021.
Notably, Software and Web Developers, Programmers, and Testers jobs accounted for a 10% share of the global travel and tourism industry’s cybersecurity-related total new job postings in Q3 2022, down 1% over the prior quarter.
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Software and Web Developers, Programmers, and Testers, with a share of 10%, emerged as the top cybersecurity-related job roles within the travel and tourism industry in Q3 2022, with new job postings drop by 1% quarter-on-quarter. Database and Network Administrators and Architects came in second with a share of 6% in Q3 2022, with new job postings drop by 5% over the previous quarter.
The other prominent cybersecurity roles include Computer and Information Analysts with a 5% share in Q3 2022, Computer and Information Systems Managers with a 4% share of new job postings.
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The top companies, in terms of number of new job postings tracked by GlobalData, as of Q3 2022 were Walt Disney, Sixt, A.P. Moller - Maersk and Marriott International. Together they accounted for a combined share of 59% of all cybersecurity-related active jobs in the travel and tourism industry.
Walt Disney posted 231 cybersecurity-related new jobs in Q3 2022, Sixt 193 jobs, A.P. Moller - Maersk 137 jobs, and Marriott International 112 jobs, according to GlobalData’s Job Analytics.
The largest share of cybersecurity-related new job postings in the travel and tourism industry in Q3 2022 was in the US with 50% followed by Germany (10%) and the UK (6%). The share represented by the US was six percentage points higher than the 44% share it accounted for in Q2 2022.
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData’s Jobs Analytics uses machine learning to uncover key insights from tracking daily job postings for thousands of companies globally. Proprietary analysis is used to group jobs into key thematic areas and granular sectors across the world’s largest industries. classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
