Autonomous vehicle perception systems rely heavily on high-quality datasets for tasks like object detection, tracking, and segmentation. This article explores the data engineering strategies that support these systems, including setting up annotation workflows, using data augmentation and synthetic generation to expand datasets, and implementing data version control to manage massive training data pipelines.