In this work, we present SynTable, a Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes. Our tool renders complex 3D scenes containing object meshes, materials, textures, lighting, and backgrounds. Metadata, including modal and amodal instance segmentation masks, occlusion masks, depth maps, and bounding boxes can be automatically generated based on user requirements. Our tool streamlines dataset generation, eliminating manual labeling, ensuring dataset quality and accuracy. We discuss our design goals, framework, and our tool's performance. We demonstrate SynTable's capabilities with a generated sample dataset to train a state-of-the-art model, UOAIS-Net. The results show significantly improved performance in Sim-to-Real transfer on the OSD-Amodal dataset. We provide this open-source, easy-to-use, photorealistic dataset generator to the academic community to advance deep learning and synthetic data generation research.