Data warehouse design is the discipline of laying out tables so analytical questions are fast, correct, and easy to ask . A well-designed enterprise data warehouse turns "what was revenue by region last quarter?" into a sub-second query; a badly-designed one turns the same question into a 30-minute, three-join, three-cell-disagrees-with-finance pile. For data-engineering interviews, the same three or four concepts — fact tables, dimension tables, grain, star schema, SCD — show up in every loop and every system-design round. This guide is a beginner-friendly walk through data warehouse design from first principles. We start with OLTP vs OLAP and why the two need fundamentally different schemas, then build out the Kimball data warehouse mental model — fact tables, dimensions, the star schema vs snowflake schema trade-off, grain, surrogate keys, slowly changing dimensions, partitioning, and the six-step design process — with worked examples and an interview-style problem in each section.…