###### Abstract  
This study aims to analyze online job postings using machine learning-based, semantic approaches and to identify the expertise roles and competencies required for big data professions. The methodology of this study employs latent Dirichlet allocation (LDA), a probabilistic topic modeling technique, to reveal hidden semantic structures within a corpus of big data job postings. As a result of our analysis, we have identified seven expertise roles, six proficiency areas, and 32 competencies (knowledge, skills, and abilities) necessary for big data professions. These positions include \"developer\", \"engineer\", \"architect\", \"analyst\", \"manager\", \"administator\", and \"consultant\". The six essential proficiency areas for big data are \"big data knowledge\", \"developer skills\", \"big data analytics\", \"cloud services\", \"soft skills\", and \"technical background\". Furthermore, the top five skills emerged as \"big data processing\", \"big data tools\", \"communication skills\", \"remote development\", and \"big data architecture\". The findings of our study indicated that the competencies required for big data careers cover a broad spectrum, including technical, analytical, developer, and soft skills. Our findings provide a competency map for big data professions, detailing the roles and skills required. It is anticipated that the findings will assist big data professionals in assessing and enhancing their competencies, businesses in meeting their big data labor force needs, and academies in customizing their big data training programs to meet industry requirements. b 2  
requires extensive knowledge, skills, and abilities [4]. Big data competencies include a variety of data-driven operational skills, such as all types of data collection, data logging, data cleansing, data analytics, data visualization, data security and privacy, scaling, deployment, empowerment, and querying [3, 5, 6]. Among the responsibilities of big data specialists, interpreting data and identifying their strategic and potent potential through the use of data mining and analytic models is the most essential responsibility. Big data expertise requires familiarity with the required software and hardware to acquire data and manage multiple data sources [1, 7, 8]. To derive all possible benefits from sources of big data, both government and commercial organizations must possess the necessary big data knowledge and skills [9]. Big data competencies are crucial skills that will play an important role in today's data-driven decision-making across all industries [3]. As a result, the need for professionals with the knowledge and skills to effectively manage and interpret big data has become a pressing issue that must be addressed. There are new career opportunities for big data professionals every day, particularly in the fields of big data processing and big data analytics [10, 11]. As the use of services and applications centered on big data has become more pervasive, the demand for qualified labor in the field of big data has steadily increased, and big data has become one of the most important sources of employment in the current labor market [4, 5, 8]. In this context, big data competency requirements provide IT professionals with diverse career opportunities [6]. For these workers, big data jobs have become increasingly tangible over the past decade. Throughout the life cycle of big data-oriented applications, big data professionals may assume various responsibilities and roles, depending on the scope and nature of their work [4, 12]. By analyzing and defining the roles (titles and areas of expertise) and responsibilities (competencies and skills) of big data professionals, it is possible to gain a better understanding of all available big data professions [4, 5]. There is a clear research gap in identifying the roles and related skills required for the most prominent big data jobs and defining the academic supply required to meet this demand.