Graph data is widely regarded as the next frontier in big data modeling for a variety of domains. Graph-based data models have been used in big data to organize messy or complicated data points based on their relationships. Graph analytic technologies for big data provide a framework for absorbing both structured and unstructured data from a variety of sources, allowing analysts to see the connections between entities in graphs and draw new conclusions. A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. KG is also known as one of the graph-based data models, which is used to model entities and relationships in many domains. In this paper, we propose a solution for constructing a graph data repository in a big data environment for career goals-based course recommendation in the Information Technology (IT) fields. Our proposed KG framework is based on the Labeled Property Graph (LPG) to represent a graph data model and has defined three major concept layers: course, career, and competency. Important entities and the semantic interactions between those things are also found in each concept layer. We also designed and implemented a fully automated application system to extract a large number of courses from MOOC (Massive Open Online Courses) resources. Our application system specifically uses deep learning-based technology to extract entities, handle data duplication, and then store them in Neo4j, in which a graph data model is developed based on our suggested KG architecture. The collected data from our system includes 14 career titles, 999 online courses, and 3022 competencies. It is a valuable experimental dataset for evaluating career goal-based course recommendation algorithms in the IT fields.