Accessing and processing huge amounts of heterogeneous and distributed data are some of the major challenges of data-intensive workflows. Traditionally, the descriptions of such workflows focus on their data flow. Nevertheless, control-flow-oriented workflow languages are increasingly adapted to the needs of data-intensive workflows. This provides a common level of abstraction for both data-intensive workflows and classical orchestration workflows, e.g., business workflows, which then enables a comprehensive optimization across all workflows. However, the problem still remains that workflows described in control-flow-oriented languages tend to be less efficient for data-intensive processes compared to specialized data-flow-oriented approaches. In this paper, we propose a new kind of optimization targeted at data-intensive workflows that are described in control-flow-oriented languages. We show how to improve efficiency of such workflows by introducing various techniques that partition the local data processing tasks to be performed during workflow execution in an improved way. These data processing tasks are either assigned to the workflow engine or to the tightly integrated local database engine. We evaluate the effectiveness of these techniques by means of various test scenarios.