Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Quick Snowflake SWE coding round, just one problem but with a twist thrown in at the end that I wasn't fully prepared for.

Questions Asked (1)

Given a list of intervals that are NOT sorted in increasing order, merge or process them accordingly (a variant of the classic interval problem with unsorted input).

Algorithms & Data Structures

Author's notes

The interviewer just dropped this on me right after the previous problem, no warmup.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: confirm that intervals are closed, what to do with touching intervals (e.g., [1,2] and [2,3]), and whether the output should be sorted. Then propose sorting by start time and merging in a single pass, analyzing time and space complexity.

Pro tip: Mention that sorting is the key insight for unsorted input, and discuss how to handle edge cases like empty input or intervals with equal start times. Also, briefly note that if the input is too large to sort in memory, an external sort or streaming approach might be needed.

1. Clarify requirements and edge cases

Ask about interval inclusivity, merging condition for touching intervals, and expected output format. Confirm constraints like input size and whether intervals can be empty.

2. Choose the right algorithm

Explain that sorting by start time enables a linear merge pass. Compare with alternatives like using a heap for streaming data, but highlight sorting as the standard efficient approach.

3. Walk through the merge logic

Describe iterating through sorted intervals, maintaining a current merged interval, and merging when the next interval's start is <= current end. Update the end to the maximum of the two ends.

4. Analyze complexity and trade-offs

State time complexity O(n log n) due to sorting, and space complexity O(n) for the output (or O(log n) if sorting in-place). Mention that without sorting, the problem is harder and may require O(n^2) comparisons.

5. Test with examples and edge cases

Run through a small example like [[1,3],[2,6],[8,10],[15,18]] to show the merge process. Also test empty input, single interval, and intervals that are already sorted or completely disjoint.

Key Points to Mention

Sorting by start time is crucial for unsorted input; without sorting, merging is inefficient.
Merge condition: if next.start <= current.end, merge by updating current.end = max(current.end, next.end).
Time complexity: O(n log n) due to sorting, plus O(n) for merging; space complexity O(n) for output.
Edge cases: empty list, single interval, intervals with same start, touching intervals (e.g., [1,2] and [2,3]).
Stability of sorting: if intervals have same start, order by end doesn't matter for correctness.
Alternative approaches: using a heap for streaming data or when intervals arrive online, but sorting is simpler for static input.

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