Fully Homomorphic Encryption (FHE) promises the ability to compute over encrypted data without revealing sensitive contents. Yet, integrating it into real-world relational databases remains elusive due to prohibitive performance overhead and the structural mismatch between mutable database records and static ciphertexts. This paper presents Hermes, a system that enables homomorphically encrypted vectorized relational queries directly inside a standard SQL engine. To bridge the relational and algebraic abstractions, Hermes introduces a SIMD-aware data model that packs multiple records per ciphertext. By embedding precomputed aggregate statistics alongside data slots, the system supports efficient rotation-free aggregations. Furthermore, to overcome ciphertext immutability, we develop data-oblivious homomorphic algorithms based on slot masking and shifting, enabling secure in-place record modifications. Hermes is implemented as native loadable functions in MySQL, marking the first practical integration of FHE into an industrial-grade relational database engine. Extensive evaluations across diverse datasets demonstrate an over 3400x increase in encryption throughput, an over 4000x speedup for tuple insertions, and a 300x acceleration for deletions when compared to conventional scalar FHE implementations.