This question evaluates a data scientist's SQL proficiency across querying (window functions, CTEs, joins, aggregation), data modeling (normalization, dimensional modeling, slowly changing dimensions), and operations (performance tuning, deployments, observability), while also assessing behavioral and leadership competencies such as mentorship, code review, and safe deployment practices. It is commonly asked to confirm practical application on analytical workloads and production safety, testing the SQL/data-engineering domain with both hands-on practical tasks and higher-level conceptual understanding of performance, migration strategies, and review processes.
Provide a concise self‑rating and concrete, real‑world examples that demonstrate your SQL proficiency across querying, data modeling, and operations. Tailor your examples to analytical workloads and production safety.
Rate your SQL proficiency in:
Briefly justify each rating (e.g., years, systems used, scale).
Describe a complex query you wrote that used any of: window functions, CTEs, conditional aggregation, or recursion. Include:
Walk through a performance optimization you owned end‑to‑end. Include:
Explain how you shipped a SQL/DDL change safely. Include:
Describe how you mentor or review others’ SQL. Include:
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