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Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Snowflake Software Engineer interview with two coding problems back to back. The web crawler question had some interesting complexity wrinkles and the bracket validator had a follow-up that pushed beyond the usual easy version.

Questions Asked (2)

Q1

Implement a web crawler function that takes seed URLs, a fetch API, a max depth limit, and a domain filter. It should do BFS, avoid revisiting URLs, respect the domain constraint, and return all discovered URLs. Also analyze the time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This one took me a minute to structure properly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a BFS-based crawler using a queue and a visited set. Discuss domain filtering, depth tracking, and complexity analysis, and consider concurrency and politeness if relevant.

Pro tip: Mention that you would use a visited set to avoid cycles and that domain filtering should be applied before enqueueing to save resources. Also, briefly discuss how you would handle rate limiting and robots.txt for a production crawler.

1. Clarify Requirements

Ask about expected scale, concurrency needs, and whether the fetch API is synchronous or asynchronous. Confirm that the domain filter applies to the hostname and that max depth is inclusive.

2. Design Data Structures

Use a queue for BFS, a set for visited URLs, and a map or tuple to track depth. Ensure the visited set is updated before enqueueing to prevent duplicates.

3. Implement BFS with Constraints

Initialize the queue with seed URLs at depth 0. While the queue is not empty, dequeue a URL, fetch its content, extract links, and for each link check domain and depth before enqueueing if not visited.

4. Analyze Complexity

Time complexity is O(N) where N is the number of URLs crawled, assuming constant time per fetch and link extraction. Space complexity is O(N) for the visited set and queue.

5. Discuss Trade-offs and Optimizations

Mention potential improvements like concurrent fetching, rate limiting, and using a distributed queue for scale. Also discuss how to handle errors and retries.

Key Points to Mention

  • BFS ensures shortest path and level-by-level crawling.
  • Visited set prevents infinite loops and duplicate work.
  • Domain filter should be applied before enqueueing to avoid unnecessary fetches.
  • Depth tracking can be done by storing depth with each URL in the queue.
  • Time complexity O(N) and space complexity O(N) where N is number of URLs.
  • Consider concurrency, rate limiting, and robots.txt for production readiness.

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

Q2

Write an algorithm to check if a string of parentheses is valid. Then extend it to handle three bracket types: '()', '[]', and '{}'. If the string is invalid, return the index of the first error. Walk through edge cases like empty strings, odd-length inputs, and early closing brackets.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Stack-based solution, pretty standard.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present a stack-based solution for the single bracket type, and extend it to multiple types with error index tracking. Walk through examples and discuss time/space complexity.

Pro tip: Mention that you can return the index of the first error by checking mismatches during stack operations, and handle early closing brackets by verifying the stack is not empty before popping.

1. Clarify requirements and edge cases

Ask if the string can be empty, contain only brackets, and what to return for valid strings. Discuss edge cases like empty string, odd length, and early closing brackets.

2. Design single bracket algorithm

Use a counter or stack to track open brackets. For each character, increment for '(' and decrement for ')'. If counter goes negative, return index; at end, if counter != 0, return index of first unmatched open bracket.

3. Extend to multiple bracket types

Use a stack to store opening brackets. For each closing bracket, check if it matches the top of the stack. If not, return current index. At end, if stack not empty, return index of first unmatched opening bracket.

4. Handle error index and edge cases

For early closing brackets, return the index immediately. For odd-length strings, return 0 or the first index where mismatch occurs. For empty string, return -1 (or as specified).

5. Analyze complexity and test

State O(n) time and O(n) space. Walk through examples: '()[]{}', '([)]', '((', ')', etc., showing returned indices.

Key Points to Mention

  • Stack data structure for matching brackets
  • Time complexity O(n) and space complexity O(n)
  • Handling empty string (return -1 or 0)
  • Odd-length strings automatically invalid
  • Early closing bracket detection by checking stack empty before pop
  • Returning index of first error by tracking position during iteration

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