Open data is an emerging paradigm to share large and diverse datasets -- primarily from governmental agencies, but also from other organizations -- with the goal to enable the exploitation of the data for societal, academic, and commercial gains. There are now already many datasets available with diverse characteristics in terms of size, encoding and structure. These datasets are often created and maintained in an ad-hoc manner. Thus, open data poses many challenges and there is a need for effective tools and techniques to manage and maintain it. In this paper we argue that software maintenance and reverse engineering have an opportunity to contribute to open data and to shape its future development. From the perspective of reverse engineering research, open data is a new artifact that serves as input for reverse engineering techniques and processes. Specific challenges of open data are document scraping, image processing, and structure/schema recognition. From the perspective of maintenance research, maintenance has to accommodate changes of open data sources by third-party providers, traceability of data transformation pipelines, and quality assurance of data and transformations. We believe that the increasing importance of open data and the research challenges that it brings with it may possibly lead to the emergence of new research streams for reverse engineering as well as for maintenance.