Window function: lead/lag over user_id

Last updated: October 31, 2025

Quick Overview

Use window functions to compute rank partitioned by user_id.

Twilio
Data Manipulation (SQL/Python)
Data Scientist
Twilio
October 31, 2025
Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Hard

22

1

3,070 solved


Use window functions to compute rank partitioned by user_id.

Data manipulation questions at Twilio test your ability to work with real-world datasets. This Phone Screen question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.

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
Pandas vectorized operations and groupby
Subqueries and correlated subqueries
Date/time manipulation
Aggregate functions and GROUP BY
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
  • What indexes would you create to support this query?
  • Can you rewrite this without using subqueries?
  • How would you optimize this query for a table with 100 million rows?
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