Scientific workflows may be used to enable the collaborative implementation of scientific applications across various domains. Since each domain has its own requirements and solutions for data handling, such workflows often have to deal with a highly heterogeneous data environment. This results in an increased complexity of workflow design. As scientists typically design their scientific workflows on their own, this complexity hinders them to concentrate on their core issue, namely the experiments, analyses, or simulations they conduct. In this paper, we present a novel approach to a pattern-based abstraction support for the complex data management in simulation workflows that goes beyond related work in similar research areas. A pattern hierarchy with different abstraction levels enables a separation of concerns according to the skills of different persons involved in workflow design. The goal is that scientists are no longer obliged to specify low-level details of data management in their workflows. We discuss the advantages of this approach and show to what extent it reduces the complexity of simulation workflow design. Furthermore, we illustrate how to map patterns onto executable workflows. Based on a prototypical implementation of three real-world simulations, we evaluate our approach according to relevant requirements.