We present SynDARin, a methodology for synthesizing high-quality reasoning datasets in low-resource languages. Our approach combines template-based generation with cross-lingual transfer techniques, enabling the creation of reasoning benchmarks for languages with limited NLP resources and improving multilingual reasoning capabilities.