Snowflake·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Snowflake SWE interview that came down to a spatial assignment problem, the kind where the naive greedy solution works fine until it doesn't. One question, but it had real depth if you let it.

Questions Asked (1)

Given a set of customers and a set of cake vendors at fixed locations, first assign each customer to the nearest vendor, then redesign the assignment globally to minimize total distance while respecting a maximum customer limit per vendor. Walk through your approach and compare the tradeoffs.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

Started with the obvious greedy pass, each customer just picks the closest vendor, O(n*m), easy to explain.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: the first phase is a nearest-neighbor assignment (greedy), and the second phase is a capacitated assignment problem that can be modeled as min-cost flow or bipartite matching. Walk through the greedy approach, then explain how to formulate the global optimization with capacity constraints, and compare tradeoffs in terms of optimality, complexity, and scalability.

Pro tip: Mention that the greedy nearest-neighbor can violate capacity constraints and may be far from optimal; the global optimization is a transportation problem solvable in polynomial time, but for large-scale systems you might need approximation or distributed algorithms.

1. Clarify the problem and constraints

Restate the problem: assign each customer to exactly one vendor, minimize total distance, and respect each vendor's maximum customer limit. Ask about input size, distance metric, and whether vendors can be unassigned.

2. Describe the greedy nearest-vendor assignment

Explain that for each customer, you pick the closest vendor. This is O(n*m) naively or O(n log m) with spatial indexing. Note that it ignores capacity and may overload vendors.

3. Formulate the global optimization

Model as a min-cost bipartite matching or transportation problem: customers on one side, vendors with capacity on the other, edge costs are distances. Use min-cost max-flow or the Hungarian algorithm with capacity splitting.

4. Compare tradeoffs

Greedy is fast but suboptimal and may violate capacity; global optimization is optimal but more complex (O(n^3) for Hungarian, or flow algorithms). Discuss scalability: for large n, use approximation algorithms or heuristics like local search.

5. Discuss practical considerations

Mention real-world factors: dynamic updates, distributed computation (e.g., Snowflake's parallel processing), and potential need for load balancing beyond distance.

Key Points to Mention

Greedy nearest-neighbor is not capacity-aware and can be arbitrarily bad.
The global problem is a capacitated assignment problem, solvable via min-cost flow or Hungarian algorithm with capacity constraints.
Time complexity: greedy O(n*m), optimal O(n^3) or O(n^2 m) with flow; space complexity considerations.
Tradeoff between optimality and scalability; approximation algorithms (e.g., greedy with capacity checks, local search) for large datasets.
Potential use of spatial data structures (KD-trees, Voronoi diagrams) to speed up nearest-neighbor queries.
Real-world extensions: dynamic customer/vendor changes, multiple objectives (distance + load balancing), and distributed optimization.

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