Notion·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Interviewed for a Data Engineer role at Notion and got a SQL window function question that was more involved than I expected. Not a bad experience but it required knowing the details, not just the concept.

Questions Asked (1)

Given a transactions or events table, write a SQL query using a window function to compute a per-row metric within partitions. For example: rank each user's purchases by date, calculate a running revenue total per customer, or find the difference between consecutive events. Also explain how PARTITION BY and ORDER BY work inside OVER, and how window functions differ from GROUP BY.

Data ModelingTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I knew window functions well enough to write the query but stumbled a bit explaining why it's different from GROUP BY.

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Suggested Approach

Start by clarifying the table schema and the exact metric needed, then write a SQL query using a window function with PARTITION BY and ORDER BY to compute the per-row metric. After presenting the query, explain how PARTITION BY and ORDER BY define the window, and contrast window functions with GROUP BY in terms of row preservation and aggregation scope.

Pro tip: Mention that window functions are evaluated after WHERE, GROUP BY, and HAVING but before ORDER BY and LIMIT, which explains why you can't filter on a window function directly in WHERE. Also, note that Notion values clean, readable SQL and an understanding of performance implications, so briefly discuss indexing on partition and order columns.

1. Clarify requirements and schema

Ask about the table structure (e.g., columns like user_id, transaction_date, amount) and confirm the exact metric (e.g., rank, running total, difference).

2. Write the SQL query

Construct a query using the appropriate window function (e.g., RANK(), SUM() OVER, LAG()) with PARTITION BY and ORDER BY clauses to compute the metric per row.

3. Explain PARTITION BY and ORDER BY

Describe how PARTITION BY divides the result set into groups (partitions) and ORDER BY defines the order of rows within each partition for the window function.

4. Contrast with GROUP BY

Explain that GROUP BY collapses rows into groups, while window functions retain individual rows and add computed columns, allowing access to both detail and aggregate data.

5. Discuss performance and edge cases

Mention indexing on partition/order columns, handling ties (e.g., RANK vs DENSE_RANK), and nulls or gaps in data.

Key Points to Mention

Window functions operate on a set of rows (window) defined by PARTITION BY and ORDER BY, without collapsing rows.
PARTITION BY groups rows into partitions; ORDER BY sorts rows within each partition for order-sensitive functions like RANK or LAG.
GROUP BY reduces the number of rows to one per group, while window functions keep all rows and add a computed column.
Common window functions: ROW_NUMBER(), RANK(), DENSE_RANK(), SUM(), AVG(), LAG(), LEAD().
Window functions are evaluated after WHERE, GROUP BY, and HAVING, so you cannot use them in WHERE; use a subquery or CTE to filter.
Performance can be improved with indexes on partition and order columns, and by avoiding unnecessary sorting.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.