Qom, a language of the Guaycuruan family, is a low-resource language for NLP and speech processing. We present the first parallel Qom–Spanish corpus in a computationally usable format, comprising 33,392 parallel segments, totaling 1,469,905 Qom tokens and 891,344 Spanish tokens. A subset of 2,943 segments excludes Bible-derived content. It includes alignments at different levels: sentences, sentence fragments, and paragraphs, and is compiled from multiple sources, both previously available and newly collected. We also present bidirectional neural machine translation baselines based on NLLB-200, achieving competitive performance in both translation directions on the full dataset, and lower performance on the non-Bible subset. An ablation study shows that training exclusively on biblical data reduces performance on non-biblical text, highlighting the importance of domain diversity in low-resource machine translation.