Computationally intensive tasks of IoT applications can be offloaded to powerful devices in the edge. Code offloading reduces energy consumption and increases performance. However, applications that use face recognition, machine learning, or image rendering, rely on large amounts of data. The transfer of this data leads to latencies which contradicts the responsiveness required by many pervasive applications. As a solution, decoupling the data from the tasks allows to apply new scheduling strategies that place data on remote devices before the actual task execution. Grid computing approaches use this technique effectively, however, edge computing introduces further challenges such as device fluctuation and heterogeneity.In this paper, we propose a data management approach for edge computing environments that decouples data placement from task scheduling. We present a multi-level scheduler, which places data on resource providers in the system considering multiple context dimensions. The scheduler allocates tasks according to the current context and observes the state during runtime. If required, the system adjusts the number of data copies to optimize the trade-off between execution latencies and data management overhead. The paper has three contributions: (1) a context-aware multi-level scheduler, (2) the integration of four data placement, three task scheduling, and three runtime adaptation algorithms, (3) an evaluation in a real-world testbed.