Advances in smartphone technologies enable mobile data subscribers to resell their data allowance to other users, creating a secondary data market. The trading environment of this secondary data market is dynamic and ad-hoc: buyers and sellers join and leave the market at all times, changing the trading landscape constantly. The amount of data demanded and offered at any point in time also vary. These conditions make determining a fair transaction price, and matching buyers to sellers difficult in practice. Prior schemes utilize global description of the network and market forces to achieve good performance, but the implementation requires a high overhead cost. In this paper, we present DataMart, a data pricing and user matching platform for trading in this dynamic, ad-hoc and heterogeneous market that works in distributed manner without needing global information. Using insights from real world traces, we demonstrate via simulation that our pricing scheme is converging and consistent with the law of demand and supply. Further, our user matching scheme achieves comparable performance to the optimal solution. We implement a prototype on Android platform, and the experiment results confirm the effectiveness of DataMart.