As a vital process to the success of an organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data, which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement. To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data. Specifically, we first construct a salary matrix based on the large-scale recruitment data and creatively formalize the salary benchmarking problem as a matrix completion task. Along this line, we develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the missing salary information in the salary matrix. Indeed, by integrating multiple confounding factors, such as company similarity, job similarity, and spatial-temporal similarity, HSBMF is able to provide a holistic and dynamic view for fine-grained salary benchmarking. Finally, extensive experiments on large-scale real-world data clearly validate the effectiveness of our approach for job salary benchmarking.