This paper presents our approach to Arabic sentiment analysis with a specific focus on dialect-awareness for Saudi and Moroccan (Darija) dialectal variants. We develop a system that achieves a macro F1 score of 77% on the test set, demonstrating effective generalization across these dialect variations. Our approach leverages a pre-trained Arabic language model (Qarib) with custom dialect-specific embeddings and preprocessing techniques tailored to each dialect. The results show a significant improvement over baseline models that do not incorporate dialect information, with an absolute gain of 5% in F1 score over the equivalent non-dialect-aware model. Our analysis further reveals distinct sentiment expression patterns between Saudi and Darija dialects, highlighting the importance of dialect-aware approaches for Arabic sentiment analysis.