Online job advertisements (OJAs) have become a significant data source for analyzing labor market dynamics, offering insights into shifts within occupations, industry sectors, skills, and tasks. This paper investigates the cross-lingual and cultural differences in OJAs and their impact on the transferability of Natural Language Processing (NLP) methods and research scope. By analyzing OJAs from Austria, France, Germany, Italy, Spain, the UK, and the US, we point out substantial variations in document length, diversity metrics, syntactic structures, and content features such as salary information. These differences underscore the challenges in applying NLP methods universally across languages and cultures. Our findings emphasize the need for tailored approaches in NLP research and offer a starting point for developing standardized pipelines for analyzing text genres across different languages.