Proposing solutions to the lack of collaboration between coursework and workplace needs, incomplete chains of practical training, and inadequate industry-academia partnership in training big data professionals at universities, this research paper develops a talent development system on the lens of deep industry-education integration. According to job-related corpus mining, competency weighting modeling, three-way mapping of course content, project tasks, competency indicators, process data collection platform practical teaching platform, and multi-source evaluation integration approaches, a cycle of implementation pathway has been developed that encompasses the design of training objectives, organization of tasks, platform support, and evaluation of quality. A comparative validation study was carried out on a sample of two grade levels of a university program on big data. The findings suggest that the designed system is more effective than conventional training models in terms of the time of task completion, passing the module tests, error rollback rate, collaborative interactions, and overall training quality indicators. In addition, students showed greater improvements in areas like engineering implementation, job fit, data governance and collaborative communication. The research shows that this system has the potential to convert job needs of the enterprise into measurable, implementable and trackable units of instruction and increase correspondence between course delivery and actual task chains in the job environment and give a viable technical route to maximize talent development models in university big data programs.
