Window function: lead/lag over user_id
Last updated: September 25, 2025
Quick Overview
Use window functions to compute rank partitioned by user_id.
Notion
Data Manipulation (SQL/Python)
Data Scientist
Notion
September 25, 2025Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Medium
215
8
2,822 solved
Use window functions to compute rank partitioned by user_id.
Data manipulation questions at Notion 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
- 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
NULL handling and COALESCE
JOIN types and when to use each
Common Table Expressions (CTEs)
Pandas vectorized operations and groupby
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- 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 this if the data was spread across multiple databases?
- What indexes would you create to support this query?
- How would you handle slowly changing dimensions in this scenario?
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Practice SQL ProblemsSample Answer
Problem Understanding
In this problem, we have a dataset that includes user interactions, identified by a unique user_id. The goal is to compute a rank for each interaction, partitioned by user_id. This means that for ...
Approach
- Identify the data structure: Determine the relevant columns needed from the dataset, such as
user_id,interaction_time, orscore. - Use a Common Table Expression (CTE): This will hel...
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