Global Navigation Satellite System (GNSS) signals are fundamental in applications across navigation, transportation, and industrial networks. However, their extremely low received power makes them highly vulnerable to radio-frequency interference (RFI) and intentional jamming. Modern data-driven methods offer powerful representational power for such applications, however real-time and reliable jamming detection on resource-limited embedded receivers remains a key challenge due to the high computational and memory demands of the conventional learning paradigm. To address these challenges, this work presents a dictionary-based contrastive learning (DBCL) framework for GNSS jamming detection that integrates transfer learning, contrastive representation learning, and model compression techniques. The framework combines tuned contrastive and dictionary-based loss functions to enhance feature separability under low-data conditions and applies structured pruning and knowledge distillation to reduce model complexity while maintaining high accuracy. Extensive evaluation across varying data regimes demonstrate that the proposed algorithm consistently outperforms modern CNN, MobileViT, and ResNet-18 architectures. The framework achieves a substantial reduction in memory footprint and inference latency, confirming its suitability for real-time, low-power GNSS interference detection on embedded platforms.