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Snowflake·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Two fairly meaty coding problems back to back for a Snowflake SWE screen. The first was a topological sort with some follow-up pressure on edge cases, and the second was a full expression evaluator with precedence rules that honestly took me by surprise in terms of scope.

Questions Asked (2)

Q1

Given n tasks labeled 0 to n-1 and a list of prerequisite pairs, return a valid ordering that completes all tasks, or an empty array if no valid ordering exists. Implement this, then walk through both BFS and DFS approaches, explain how you detect cycles, discuss complexity, and talk about how you'd handle streaming edges or multiple valid orderings.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I went straight to BFS with in-degree tracking which felt natural, but then they asked me to also sketch the DFS version and I fumbled explaining the three-color visited state.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then implement Kahn's algorithm (BFS) as the primary solution. Walk through both BFS and DFS approaches, emphasizing cycle detection and complexity. Finally, discuss trade-offs and extensions like streaming edges and multiple valid orderings.

Pro tip: Mention that Kahn's algorithm naturally detects cycles by checking if the processed count equals n, and that DFS uses recursion stack states. Also, note that the problem is equivalent to topological sorting, and Snowflake values scalable solutions for large DAGs.

1. Clarify requirements and edge cases

Ask about input constraints (e.g., n size, duplicate edges, self-loops) and output format. Confirm that any valid ordering is acceptable and that an empty array indicates a cycle.

2. Implement BFS (Kahn's algorithm)

Build adjacency list and indegree array, enqueue nodes with indegree 0, then process queue while decrementing indegrees. If result length equals n, return ordering; else return empty array.

3. Implement DFS with cycle detection

Use DFS with three states (unvisited, visiting, visited) to detect cycles. Perform post-order traversal to build topological order, reversing the result at the end.

4. Analyze complexity and trade-offs

Both approaches are O(V+E) time and O(V+E) space. BFS is iterative and easier to reason about for cycle detection; DFS can be more intuitive for some but risks stack overflow for large graphs.

5. Discuss extensions and real-world considerations

For streaming edges, consider incremental topological sort or dynamic algorithms. For multiple valid orderings, note that any topological order is acceptable, but if a specific order is needed, use a priority queue (e.g., lexicographically smallest).

Key Points to Mention

  • Topological sorting is only possible for Directed Acyclic Graphs (DAGs).
  • Kahn's algorithm (BFS) detects cycles by comparing the number of processed nodes to n.
  • DFS cycle detection uses a recursion stack or color marking (white, gray, black).
  • Time and space complexity for both approaches is O(V+E).
  • For multiple valid orderings, any topological order is valid; for a specific order, use a min-heap.
  • Streaming edges require dynamic topological sort algorithms, which are more complex and may not always be feasible.

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

Q2

Implement a function that parses and evaluates an arithmetic expression string supporting integers, spaces, parentheses, unary minus, and the operators +, -, *, /, %, and ^. Exponentiation is highest precedence and right-associative; division truncates toward zero; use 64-bit integers; raise an error on malformed input. Target O(n) time and space.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one blindsided me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a two-phase solution: tokenize the input and evaluate using a precedence-aware parser (e.g., shunting-yard or recursive descent) that handles unary minus and right-associative exponentiation. Emphasize O(n) time and space, 64-bit integer arithmetic with truncation toward zero, and robust error handling for malformed input.

Pro tip: Mention that unary minus can be treated as a special operator with high precedence (but lower than exponentiation) and that you can avoid recursion depth issues by using an explicit stack; also note that division truncation toward zero differs from floor division, so use integer division carefully.

1. Clarify requirements and edge cases

Ask about operator precedence, associativity, unary minus handling, integer overflow, division truncation, and error conditions. Confirm that spaces are ignored and that parentheses are balanced.

2. Design tokenizer and parser

Tokenize the string into numbers, operators, and parentheses. Choose a parsing strategy: recursive descent with precedence climbing or shunting-yard with an output queue and operator stack.

3. Implement evaluation with precedence and associativity

Handle unary minus by converting it to a special token or by tracking context. Ensure exponentiation is right-associative and has highest precedence. Use 64-bit integers and truncate division toward zero.

4. Add error handling and validate input

Detect malformed input such as mismatched parentheses, invalid characters, consecutive operators, or division by zero. Raise clear errors.

5. Analyze complexity and test

Confirm O(n) time and space. Walk through examples including nested parentheses, unary minus, and mixed operators. Discuss potential overflow and how to handle it.

Key Points to Mention

  • Operator precedence and associativity: ^ highest and right-associative, unary minus, then *, /, %, then +, -.
  • Handling unary minus: distinguish from binary minus, e.g., by context or by treating as a high-precedence operator.
  • Integer arithmetic: use 64-bit integers, division truncates toward zero (C++/Java style), and consider overflow behavior.
  • Parsing algorithm: recursive descent with precedence climbing or shunting-yard, both achieving O(n) time.
  • Error handling: malformed input detection (e.g., unbalanced parentheses, invalid tokens, division by zero).
  • Space complexity: O(n) due to token storage or recursion stack; can be optimized with iterative parsing.

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