Pinterest developed an Analytics Agent to improve its Text-to-SQL capabilities for its vast data warehouse. They faced challenges due to the scale and complexity of their data, with numerous tables and diverse analytical needs. The agent leverages unified context-intent embeddings to capture the meaning behind queries, ensuring semantic understanding. Simultaneously, it extracts structural, statistical patterns and incorporates governance metadata to rank results. The data warehouse initially needed cleanup and standardization, which led to a table governance program with tiered classifications. Analytical knowledge is encoded from query history, moving beyond simple keyword matching. SQL queries are translated into natural language descriptions, capturing the original analytical intent through a three-step process. Generalizable descriptions and analytical questions create a reusable knowledge base. This natural-language description is then embedded into a vector representation for intent-based retrieval. Structural and statistical patterns are also extracted, including join and aggregation patterns. These patterns combine with governance metadata to inform a governance-aware ranking system. The agent utilizes these two dimensions to provide the necessary information for generating and validating answers to analytics question.