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Sig·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026Remote

Summary

Second part of a virtual onsite at Sig for a software engineer role. The interviewer was relaxed but the follow-ups on the ticker trie problem had some teeth. Worth knowing they leave you alone for 30 minutes to code, which is a different format than most places.

Questions Asked (1)

Q1

Design and implement a trie data structure for storing and querying stock tickers, then handle follow-up variations on the core problem.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The base trie part felt manageable but the follow-ups are where things got interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., case sensitivity, character set, operations needed) and then design a trie with nodes containing children pointers and an is_end flag. Implement core operations (insert, search, startsWith) and discuss trade-offs like memory vs. speed, then adapt the design for follow-up variations such as prefix counting or wildcard matching.

Pro tip: Mention that real stock tickers are often uppercase and limited to A-Z, so you can use a fixed-size array of 26 pointers per node for O(1) child access, but be prepared to discuss memory trade-offs and alternative representations like hash maps for sparse data.

1. Clarify Requirements and Constraints

Ask about the character set (e.g., uppercase letters only), expected operations (insert, search, prefix search), and any follow-up variations (e.g., wildcard, count prefixes). This ensures you design the right structure.

2. Design the Trie Node and Structure

Define a TrieNode class with children (array or hash map) and an is_end boolean. Explain the choice based on character set and memory considerations.

3. Implement Core Operations

Write methods for insert, search (exact match), and startsWith (prefix match). Walk through the logic step-by-step, ensuring edge cases like empty strings are handled.

4. Analyze Complexity and Trade-offs

Discuss time and space complexity: O(L) for operations where L is word length, and memory usage proportional to total characters. Compare with alternatives like hash maps or sorted arrays.

5. Handle Follow-up Variations

Adapt the trie for variations: e.g., add a count at each node for prefix counting, implement wildcard search with DFS, or support deletion. Explain modifications clearly.

Key Points to Mention

  • Choice of children representation: fixed-size array (fast, memory-heavy) vs. hash map (flexible, memory-efficient for sparse data).
  • Time complexity: O(L) for insert/search/startsWith, where L is the length of the ticker.
  • Space complexity: O(N * L) worst case, but can be optimized with compression (e.g., radix tree).
  • Handling of edge cases: empty string, duplicate insertions, and case sensitivity.
  • Follow-up variations: prefix counting (store count at each node), wildcard matching (DFS with '.'), and deletion (recursive with pruning).
  • Real-world considerations: stock tickers are short and uppercase, so a trie may be overkill; discuss when a hash set suffices.

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