Big Data Knowledge Engineering is the infrastructure of artificial intelligence, a common requirement faced by various industries and fields, and the inevitable path for the digitalization to intelligence. In this paper, we firstly elaborate on the background and connotation of big data knowledge engineering and propose a research framework of "data knowledgeization, knowledge systematization, and knowledge reasoning". Secondly, we sort out the key technologies of knowledge acquisition and fusion, knowledge representation, and knowledge reasoning and introduce engineering applications in typical scenarios such as smart education, tax risk control, and smart healthcare. Thirdly, we summary the challenges faced by big data knowledge engineering and predict the future research directions including complex big data knowledge acquisition, knowledge+data hybrid learning, and brain-inspired knowledge coding and memorizing. Finally, several suggestions are given by the research: guiding interdisciplinary integration and establishing major and key R&D projects to promote the basic theory and technological breakthroughs of big data knowledge engineering; strengthening communication and cooperation between enterprises and research institutions as well as promoting cutting-edge research results to form application demonstrations, so as to establish an industry-standard system for big data knowledge engineering; exploring school-enterprise cooperation in line with market demands, orienting towards major application needs, and accelerating the landing application of big data knowledge engineering technology in the country's important industries.