In Amazon’s SQL interviews, the focus is on data reasoning and crafting clean, correct queries under pressure, with candidates verbalizing their thought process and defining metrics aloud before coding.

Cohort analyses and rolling window calculations are common, so practice DATE_TRUNC, date differences, and rolling aggregations.

Avoid common traps: clarify the metric before coding, modularize with CTEs rather than a single large query, account for duplicates and nulls, and keep thinking-aloud without long silences while verifying data grain and edge cases.

Use a simple interview framework: restate the problem, discuss approach aloud, implement in logical blocks with CTEs, run sanity checks, and articulate tradeoffs for efficiency or simplicity.

Interviews may be technical screens or whiteboard cases, prioritizing correctness, clear communication, and handling messy data including nulls, duplicates, and grain mismatches over raw speed.

Mastery should cover JOIN types (INNER, LEFT, RIGHT, FULL), window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG/LEAD), aggregations, grouping logic, and the distinction between HAVING and WHERE.

Prefer Common Table Expressions (CTEs) over nested subqueries to keep queries readable and to support explaining reasoning during the interview.

Practice guidance emphasizes using real Amazon-tagged questions, working with messy data, timing yourself to write a correct and readable query within 30–45 minutes (aiming for under 15 minutes to draft), and vocalizing reasoning to build fluency.

Handling nulls and duplicates is a frequent hidden trap; candidates should recognize and address these issues rather than focusing solely on syntax.