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Walmart Labs·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

System design round at Walmart Labs for a software engineer role. The whole thing was centered on one big problem about temperature sensors and it went deeper than I expected, covering a lot of ground across storage, streaming, and spatial stuff.

Questions Asked (1)

Q1

Design a system that ingests data from a statewide network of temperature sensors (one per N square feet) and supports three use cases: fetching the latest reading for a specific sensor, returning the top-K hottest sensors globally right now, and rendering a statewide heat map.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

This was the entire interview basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: sensor density, update frequency, latency needs, and scale (statewide). Then propose a high-level architecture with ingestion, storage, and serving layers, and dive into data structures and trade-offs for each use case.

Pro tip: Demonstrate awareness of real-world constraints like network partitions and sensor failures by designing for graceful degradation and eventual consistency where appropriate.

1. Clarify Requirements and Scale

Ask about number of sensors, update frequency, expected read patterns, latency requirements, and consistency needs. Estimate data volume and QPS.

2. High-Level Architecture

Sketch components: ingestion pipeline (e.g., Kafka), storage (time-series DB, cache, search index), and serving layer (API, map rendering).

3. Design for Each Use Case

For latest reading: key-value store with sensor ID. For top-K: maintain a heap or sorted set updated on ingestion. For heat map: pre-aggregate into grid cells and serve via tile server.

4. Deep Dive into Trade-offs

Discuss consistency vs. latency, push vs. pull for top-K, storage costs, and partitioning strategies. Consider using approximate algorithms for top-K if exactness isn't critical.

5. Address Reliability and Scalability

Explain how to handle sensor failures, data duplication, and scaling horizontally. Mention monitoring, alerting, and backpressure.

Key Points to Mention

  • Use of time-series database (e.g., InfluxDB) for efficient storage and querying of sensor data.
  • Caching latest readings in Redis for low-latency access.
  • Maintaining a global top-K using a distributed heap or sorted set with periodic updates.
  • Heat map generation via pre-aggregated grid cells and serving as map tiles (e.g., using GeoJSON or vector tiles).
  • Partitioning strategy: shard by sensor ID or geographic region to distribute load.
  • Trade-offs between exact and approximate top-K (e.g., using count-min sketch or t-digest).

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