Calculate engagement score per region

Last updated: August 29, 2025

Quick Overview

Write a query to compute engagement score grouped by user, handling edge cases like nulls and duplicates.

Elastic
Data Manipulation (SQL/Python)
Data Scientist
Elastic
August 29, 2025
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Medium

28

6

4,408 solved


Write a query to compute engagement score grouped by user, handling edge cases like nulls and duplicates.

Elastic asks this during the Onsite because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.

What the Interviewer Expects
  • Use advanced SQL features: window functions, CTEs, subqueries
  • Write efficient queries that avoid common performance pitfalls
  • Handle complex data transformations with multiple joins and aggregations
  • Discuss indexing strategy and query optimization
  • Address data quality issues: duplicates, missing values, outliers
Key Topics to Cover
Subqueries and correlated subqueries
Common Table Expressions (CTEs)
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
JOIN types and when to use each
Data cleaning and transformation
Date/time manipulation
How to Approach This
  1. Clarify the schema and expected output format before writing queries.
  2. Use CTEs (WITH clauses) to break complex queries into readable steps.
  3. Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
  4. Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
  5. For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
  • How would you handle this if the data was spread across multiple databases?
  • How would you optimize this query for a table with 100 million rows?
  • What indexes would you create to support this query?
  • How would you validate the correctness of your query results?
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