Scale AI·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Scale AI SWE interview with a data engineering problem that looked like a SQL question but turned into more of an algorithms exercise once you got into the merging logic. Pretty clean problem overall, though the edge cases kept me on my toes.

Questions Asked (1)

You have two tables: one with party records including start and end timestamps, and one mapping each party to a neighborhood, city, and state. For each neighborhood, merge overlapping or touching time intervals into maximal continuous blocks and return the results sorted by state, city, neighborhood, then block start time.

Algorithms & Data StructuresData ModelingSystem Design

Author's notes

The join part was fine, group by neighborhood and pull all the intervals together.

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AI HintsAI Generated

Suggested Approach

First, join the party records with the neighborhood mapping to associate each party with its location. Then, for each neighborhood, sort the intervals by start time and merge overlapping or touching intervals into maximal continuous blocks. Finally, sort the resulting blocks by state, city, neighborhood, and block start time.

Pro tip: Clarify whether 'touching' intervals (where one ends exactly when the next begins) should be merged, as this can affect the output. Also, consider the scale of data and discuss efficient algorithms (e.g., O(n log n) sorting) and whether a single-pass merge is feasible.

1. Understand the data and requirements

Identify the two tables: party records with start/end timestamps and a mapping table with neighborhood, city, state. Clarify that intervals are per neighborhood and that overlapping or touching intervals must be merged.

2. Join tables and group by neighborhood

Perform an inner join on party ID to combine timestamps with location data. Group records by neighborhood (and implicitly state and city) to process intervals per neighborhood.

3. Sort intervals and merge overlapping/touching

For each neighborhood, sort intervals by start time. Iterate through sorted intervals, merging the current interval with the next if they overlap or touch (i.e., next.start <= current.end).

4. Sort final blocks and return

After merging, sort the resulting blocks by state, city, neighborhood, and block start time. Return the sorted list of blocks.

Key Points to Mention

Handling of overlapping vs. touching intervals: define merge condition as next.start <= current.end.
Efficiency: sorting intervals per neighborhood takes O(n log n) time; merging is O(n).
Edge cases: empty tables, intervals with same start/end, intervals that are completely contained within others.
Data modeling: ensure proper indexing on join keys and consider partitioning by neighborhood for scalability.
Output format: specify columns (state, city, neighborhood, block_start, block_end) and sorting order.
Potential for using window functions or SQL to perform the merge if working in a database context.

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