Figma data engineering interview questions lean on a narrow, high-fluency stack: SQL that ranks and dedupes per entity with ROW_NUMBER() OVER (PARTITION BY creator_id ORDER BY collab_count DESC, last_collab_at DESC) , aggregation joins that pull "first event per entity" with MIN(shared_at) plus LEFT JOIN so creators with zero shares survive, and vanilla Python that splits and validates a structured string with str.split('.') , str.isdigit() , int() , and a leading-zero guard — no re , no ipaddress , no pandas . The schema you reason over feels like Figma's own product ( creators , files , shares , collaborators ), and the bar is fluency with window-function tie-breaks , NULL -safe LEFT JOIN aggregation , and structural-then-per-element validation . This guide walks through the four topic clusters Figma actually tests, each with a detailed topic explanation , per-sub-topic explanation with a worked example and its solution , and an interview-style problem with a full solution that explains why it works.…