SQL Interview Questions Answered! How to Nail a SQL Interview

Orders table, some rows written twice because a retry fired upstream. Give me one row per order, keep the most recent. You have about ten seconds before the interviewer forms an opinion, and reaching for GROUP BY is how most people lose it. These are the 10 SQL questions that come up most in data engineering loops, and they are not the ones an analyst gets. An analyst interview checks whether you can turn a business question into a correct query. A data engineering interview checks whether your query is still correct when the data arrives dirty, duplicated, late, or a thousand times bigger than the sample. I cover the fan-out trap that inflates revenue, row_number vs rank vs dense_rank, three ways to deduplicate and why two of them are wrong, the tiebreaker that makes your pipeline reproducible, gaps and islands in a single subtraction, incremental loads that quietly lose data, and SCD type 2. Everything runs on DuckDB against a tiny dataset so you can follow along without installing anything heavy. ⏱️ Chapters 0:00 - The duplicate orders question 1:01 - Analyst SQL vs data engineering SQL 1:58 - What's actually on the scorecard 3:00 - The fan-out trap 4:29 - When rows disappear instead 5:09 - row_number vs rank vs dense_rank 6:20 - PARTITION BY vs GROUP BY 7:07 - Deduplication: why GROUP BY fails 8:18 - QUALIFY and which engines have it 8:59 - The tiebreaker nobody adds 9:59 - Gaps and islands in one subtraction 11:29 - Incremental loads and watermarks 12:57 - Slowly changing dimension type 2 13:41 - What to do when you don't know 🔗 Links DuckDB: https://duckdb.org #SQL #dataengineering #interviewprep #DuckDB #windowfunctions #rownumber #gapsandislands #deduplication #SCD #slowlychangingdimensions #dataengineerinterview #SQLinterview #incrementalload #idempotency #analytics