rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Rippling software engineering interview with a data aggregation problem that looked deceptively simple at first glance. The constraint about a million records is what made it interesting, since a naive approach would get wrecked on repeated full scans.

Questions Asked (1)

Given a list of expense records (each with an employee ID, trip ID, category, and amount), implement functions to compute: total spend per employee across all records, total spend per trip for a given employee, and total spend per category for a given employee. The solution needs to handle up to a million records efficiently without re-scanning the full list on every query.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

My first instinct was to just filter and sum on each call, which works fine for small inputs but obviously falls apart at scale.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a precomputation strategy using hash maps to aggregate totals per employee, per trip per employee, and per category per employee. Discuss time and space complexity, and consider edge cases like missing data or duplicate records.

Pro tip: Mention that you would use a single pass to build all aggregates, and consider using a composite key (employeeId, tripId) and (employeeId, category) to avoid nested maps. Also, discuss how you would handle updates or additional queries without full rescans.

1. Clarify requirements and constraints

Ask about data size, query frequency, update frequency, and whether the data is static or dynamic. Confirm the exact output format and any edge cases.

2. Design data structures for precomputation

Propose using hash maps: one for total per employee, one for total per (employee, trip), and one for total per (employee, category). Explain that this allows O(1) query time after O(n) preprocessing.

3. Implement aggregation in a single pass

Iterate through the records once, updating all three maps simultaneously. This avoids multiple scans and is efficient for large datasets.

4. Analyze complexity and trade-offs

Discuss time complexity O(n) for preprocessing and O(1) for queries, space complexity O(k) where k is number of unique keys. Compare with alternative approaches like on-the-fly computation or database indexing.

5. Address edge cases and extensions

Handle missing or invalid data, duplicate records, and potential memory constraints. Discuss how to extend to dynamic updates or additional query types.

Key Points to Mention

Use of hash maps for O(1) average-time lookups and updates.
Single-pass aggregation to minimize time complexity.
Composite keys (employeeId, tripId) and (employeeId, category) to avoid nested maps.
Time and space complexity analysis: O(n) preprocessing, O(1) queries, O(k) space.
Trade-offs between precomputation and on-demand computation, especially for dynamic data.
Handling of edge cases such as missing values, duplicates, and memory limitations.

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