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Robinhood·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Robinhood data engineering interview with a Python session-splitting problem. The question was well-scoped but had enough edge cases to trip you up if you weren't careful about the single-event session rule.

Questions Asked (1)

Q1

Given a CSV log file with user_id, timestamp, and topic columns, write a Python function that groups each user's events into sessions (new session when gap exceeds 30 minutes), then outputs per-session aggregates: session start, session end, distinct topic count, and total event count. For single-event sessions, session_end should be set to session_start plus 30 minutes.

Algorithms & Data StructuresProduct Analytics & MetricsData Modeling
Author's notes

The core sessionization logic wasn't too bad once I sorted by timestamp and iterated through the gaps.

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AI HintsAI Generated

Suggested Approach

First, clarify the input format and edge cases (e.g., unsorted data, timezone handling, single-event sessions). Then, outline a solution that sorts events by user and timestamp, iterates to assign session IDs based on the 30-minute gap, and aggregates per session. Finally, discuss complexity and potential optimizations.

Pro tip: Mention that you'd use a streaming approach if the file is large, but for simplicity, sorting is fine. Also, explicitly handle the single-event session rule by setting session_end = session_start + 30 minutes.

1. Clarify requirements and edge cases

Ask about input assumptions: Is the CSV sorted? How to handle missing values? What timestamp format? Confirm that session_end for single-event sessions is start + 30 minutes.

2. Outline algorithm

Describe sorting by user_id and timestamp, then iterating to detect gaps > 30 minutes to start new sessions. For each session, track start, end, distinct topics, and event count.

3. Implement aggregation logic

Explain how to compute distinct topic count (e.g., using a set) and total event count. Update session_end to the last event's timestamp, except for single-event sessions where it's start + 30 minutes.

4. Analyze complexity and optimizations

State time complexity O(n log n) due to sorting, and space O(n). Mention potential streaming approach if data is too large to fit in memory.

5. Test with examples

Walk through a small example to verify correctness, including a single-event session and a session with multiple events.

Key Points to Mention

  • Sorting by user_id and timestamp is necessary to process sessions sequentially.
  • Session boundary defined by gap > 30 minutes between consecutive events.
  • For single-event sessions, session_end = session_start + 30 minutes.
  • Use a set to track distinct topics per session.
  • Time complexity O(n log n) due to sorting; space O(n) for storing events or O(k) for streaming.
  • Consider timezone consistency and timestamp parsing (e.g., using datetime).

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