A major Canadian freight railroad, operating roughly 20,000 route miles and moving over C$250 billion in goods annually, modernized its data estate using Databricks Genie Code, Unity Catalog, custom Agent Skills, and a Streamlit app on Databricks Apps. The company faced hundreds of pipelines, demand for real-time analytics and AI, and legacy system knowledge, requiring a scalable modernization approach. Previously, building a single pipeline took multiple days, involving schema inspection, Source-to-Target Mapping spreadsheets, historical and streaming ingestion logic, incremental merges, and tests for schema evolution and soft deletes. The new solution turns pipeline development into a repeatable factory: a short YAML prompt generates production-ready ingestion code grounded in live catalog metadata, including table definitions, historical load logic, streaming ingestion, incremental merge logic, and automated tests. The result is over 90% automation for new table ingestion, with pipeline delivery compressed from days to minutes. The modernization program now scales with business demand rather than developer bandwidth, addressing a decades-old estate built across mainframes, legacy warehouses, and ETL platforms.