Discourse relation parsing plays a crucial role in uncovering the logical structure of text, yet existing corpora focus almost exclusively on general-domain genres, leaving specialized fields like engineering under-resourced. We introduce ENG‐DRB, the first PDTB‐style discourse relation corpus derived from transcripts of hands‐on engineering tutorial videos. ENG‐DRB comprises 11 tutorials spanning civil, mechanical, and electrical/electronics engineering (155 minutes total) with 1,215 annotated relations. Compared to general‐domain benchmarks, this dataset features a high proportion of explicit senses, dense causal and temporal relations, and frequent overlapping and embedded senses. Our benchmarking experiments underscore the dataset’s difficulty. A top parser (HITS) detects segment boundaries well (98.6% F1), but its relation classification is more than 11 F1 percentages lower than on the standard PDTB. In addition, state‐of‐the‐art LLMs (OpenAI o4‐mini, Claude 3.7, LLaMA‐3.1) achieve at best 41% F1 on explicit relations and less than 9% F1 on implicit relations, revealing systematic errors in temporal and causal sense detection. The dataset can be accessed at: https://doi.org/10.57967/hf/6895. Code to reproduce our results is available at: https://github.com/chengzhangedu/ENG-DRB.