Nextdoor·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Coding screen at Nextdoor for a software engineer role. One problem, interval merging but with a twist on the input format that tripped me up more than I expected.

Questions Asked (1)

Given a list of time intervals expressed as day-of-week plus 24-hour clock timestamps (e.g. 'Mon 09:00'), write a function that merges all overlapping intervals and returns them in chronological order.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The merging logic itself is pretty standard but I lost a few minutes fumbling with the parsing step.

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AI HintsAI Generated

Suggested Approach

First, clarify the input format and edge cases, then convert each interval to a comparable numeric representation (e.g., minutes since Monday 00:00). Sort intervals by start time and merge overlapping ones by comparing each interval's start with the current merged interval's end, handling wrap-around if necessary.

Pro tip: Explicitly discuss how you would handle intervals that cross midnight or the week boundary (e.g., 'Sun 23:00' to 'Mon 01:00'), as this is a common pitfall and shows attention to real-world data.

1. Clarify requirements and edge cases

Ask about input format, whether intervals are inclusive/exclusive, if they can span multiple days, and if wrap-around across week boundaries is possible.

2. Normalize intervals to a linear timeline

Convert each day-of-week and time to a single numeric value (e.g., minutes since Monday 00:00) to simplify comparisons and sorting.

3. Sort intervals by start time

Sort the normalized intervals by their start time; if two have the same start, sort by end time.

4. Merge overlapping intervals

Iterate through sorted intervals, merging with the last merged interval if the current start is less than or equal to the last end; otherwise, add it to the result.

5. Handle wrap-around and convert back

If intervals can cross the week boundary, split them into two or adjust the merge logic; then convert merged numeric intervals back to the original format.

Key Points to Mention

Time complexity: O(n log n) due to sorting, with O(n) for merging.
Space complexity: O(n) for storing the result (or O(1) extra if merging in-place).
Edge cases: empty list, single interval, intervals that touch but don't overlap, intervals that span midnight or the week boundary.
Choice of normalization: minutes since start of week vs. seconds, and how to handle day-of-week strings.
Trade-offs: sorting vs. using a sweep line or interval tree for different scenarios (e.g., streaming data).
Testing strategy: unit tests covering normal, boundary, and wrap-around cases.

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