Window function: lead/lag over date
Last updated: October 2, 2025
Quick Overview
Use window functions to compute rank partitioned by date.
LinkedIn
Data Manipulation (SQL/Python)
Data Scientist
Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Hard
42
6
4,622 solved
Use window functions to compute rank partitioned by date.
Data manipulation questions at LinkedIn test your ability to work with real-world datasets. This Technical 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
NULL handling and COALESCE
Common Table Expressions (CTEs)
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Aggregate functions and GROUP BY
Date/time manipulation
JOIN types and when to use each
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
- What would you do if this query needs to run every 5 minutes?
- 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?
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Practice SQL ProblemsSample Answer
Problem Understanding
The problem involves a dataset that likely contains user activity logs or posts on LinkedIn, including a 'date' field. The goal is to compute a ranking of these activities partitioned by date. Each ac...
Approach
- Identify the Dataset: Start by selecting the relevant fields from the table (e.g., user_id, activity_id, activity_date).
- Define the Window: Use a window function to partition by the ac...
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