Join orders and impressions to find engagement score

Last updated: April 7, 2026

Quick Overview

Write a query joining orders and events to produce the combined engagement score.

PayPal
Data Manipulation (SQL/Python)
Data Scientist
PayPal
April 7, 2026
Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard

5

4

4,915 solved


Write a query joining orders and events to produce the combined engagement score.

This question from PayPal's Onsite tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.

What the Interviewer Expects
  • Solve complex analytical problems with elegant, readable SQL
  • Optimize queries for large-scale datasets with partitioning and indexing
  • Use recursive CTEs, lateral joins, and advanced window functions
  • Design the data model alongside the query solution
  • Discuss trade-offs between SQL and programmatic approaches (Python/pandas)
  • Consider the operational aspects: query scheduling, incremental processing
Key Topics to Cover
JOIN types and when to use each
Date/time manipulation
Data cleaning and transformation
NULL handling and COALESCE
Pandas vectorized operations and groupby
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
  • Can you rewrite this without using subqueries?
  • How would you handle slowly changing dimensions in this scenario?
  • What indexes would you create to support this query?
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