The paper proposes a modular relational deep learning approach that decouples row encoding from graph message-passing. It introduces a transformer-based Universal Row Encoder that uses schema metadata to generate invariant row embeddings, enabling better generalization across databases and improving convergence on RelBench benchmarks.