Data forms an essential organizational asset and is a potential source for competitive advantages. To exploit these advantages, the engineering of data-intensive applications is becoming increasingly important. Yet, the professional development of such applications is still in its infancy and a practical engineering approach is necessary to reach the next maturity level. Therefore, resources and frameworks that bridge the gaps between theory and practice are required. In this study, we developed a data engineering reference model (DERM), which outlines the important building-blocks for handling data along the data lifecycle. For the creation of the model, we conducted a systematic literature review on data lifecycles to find commonalities between these models and derive an abstract meta-model. We successfully validated our model by matching it with established data engineering topics. Using the model derived six research gaps that need further attention for establishing a practically-grounded engineering process. Our model will furthermore contribute to a more profound development process within organizations and create a common ground for communication.

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