← Notion Interview Insights

Notion·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

SQL-heavy technical screen for a Data Engineer role at Notion. The main problem was a multi-part query that built on itself, and the trick was figuring out why you can't just collapse everything into one GROUP BY.

Questions Asked (1)

Q1

Given a multi-part SQL problem where each stage filters on the result of the previous one, how would you use boolean aggregation (like BOOL_OR) to flag groups, then chain those flags across sub-queries to arrive at a final metric? Also, why doesn't a single GROUP BY pass work here?

Data ModelingTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This took me longer than I'd like to admit to structure properly.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by explaining the general pattern: use boolean aggregation (e.g., BOOL_OR) within a GROUP BY to create flags per group, then chain those flags in subsequent sub-queries to progressively filter and compute the final metric. Emphasize why a single GROUP BY pass fails: because each stage's filter depends on the aggregated result of the previous stage, requiring sequential evaluation.

Pro tip: Mention that while multiple passes are logically necessary, you can sometimes optimize by using window functions or CTEs to avoid redundant scans, but clarity and correctness should come first in an interview.

1. Clarify the multi-stage filtering

Restate the problem to ensure you understand each stage's filter condition and how they depend on previous results. Confirm that the final metric requires chaining flags across stages.

2. Explain boolean aggregation for flagging

Describe how BOOL_OR (or MAX for booleans) within a GROUP BY can create a flag per group indicating whether any row in the group meets a condition. This flag becomes the input for the next stage.

3. Chain flags across sub-queries

Show how to use sub-queries or CTEs to pass the flags from one stage to the next, applying additional filters and aggregations at each step. Emphasize that each stage operates on the aggregated output of the previous stage.

4. Explain why single GROUP BY fails

Highlight that a single GROUP BY cannot handle sequential dependencies because the filter at stage N depends on the aggregated result of stage N-1, which isn't available until after the GROUP BY. Thus, multiple passes are required.

5. Discuss trade-offs and optimizations

Mention potential performance considerations and alternatives like window functions or recursive CTEs, but stress that the multi-pass approach is conceptually clear and often necessary.

Key Points to Mention

  • BOOL_OR aggregates boolean values across a group, returning true if any value is true.
  • Chaining flags requires sub-queries or CTEs to carry intermediate results.
  • Single GROUP BY cannot express sequential dependencies because aggregation collapses rows and loses detail needed for later stages.
  • Use of CTEs improves readability and allows step-by-step logic.
  • Performance trade-offs: multiple scans vs. window functions, but correctness first.
  • Real-world example: filtering users who performed action A, then among them those who performed action B, etc.

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