Large scale geo-raster data have been accumulated all over the world in different departments and organizations during the past decades, but quite often in a variety of data formats, resulting in geospatial data sharing as an everlasting headache. Despite of various methodologies created, geospatial data conversion has always been a fundamental and efficient way for geospatial data sharing. However, as the size of data tends to be larger and larger, the methodology which was bounded by limited disk data transfer rate and bandwidth, needs a re-write and up-grade. A parallel geo-raster data conversion engine (PGRCE) was proposed to deal with massive geo-raster data sharing efficiently by utilizing high performance computing technologies. PGRCE was designed in an extendable and flexible framework, and was capable of customizing the way of reading and writing of particular spatial data formats. An experiment, in which georaster data in the CNSDTF-DEM format (Raster spatial data defined in Chinese Geospatial Data Transfer Format Standard) were transferred using PGRCE in a parallel file system (Lustre), were conducted to validate the engine framework and its performance. Results show that PGRCE can achieve a 7.54 speedup on a Luster cluster of 8 nodes.