← Walmart Labs Interview Insights

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

SeniorPrefer not to say
Apr 2026

Summary

System design round at Walmart Labs for a backend role. One question, but it had enough moving parts to keep me busy for the whole session.

Questions Asked (1)

Q1

Design a taxi recommendation system for airport passengers that accounts for flight schedules, layovers, arrival airports, and departure locations.

System DesignData ModelingAPI & Integrations
Author's notes

I went straight to the data model and kind of forgot to scope the problem first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and requirements with the interviewer, then outline a high-level system architecture that integrates flight data, user location, and taxi availability. Dive into key components like data modeling, API design, and matching algorithms, while discussing trade-offs and scalability.

Pro tip: Emphasize real-time data processing and fault tolerance, as flight delays and cancellations are common; propose a fallback mechanism to handle such disruptions gracefully.

1. Clarify Requirements and Scope

Ask questions to understand the system's boundaries: Is it for a single airport or multiple? What data sources are available? What are the latency and accuracy requirements? This ensures you focus on the right aspects.

2. High-Level Architecture

Sketch the main components: data ingestion (flight schedules, user locations, taxi availability), processing (matching algorithm), and output (recommendations to users). Discuss how they interact and scale.

3. Data Modeling and APIs

Define key entities (Flight, Passenger, Taxi, Airport) and their relationships. Design APIs for querying flight status, updating locations, and requesting recommendations, considering REST or GraphQL.

4. Matching Algorithm and Real-Time Processing

Explain how to match passengers to taxis based on factors like arrival time, location, and taxi availability. Discuss using streaming (e.g., Kafka) for real-time updates and handling flight delays.

5. Scalability, Reliability, and Trade-offs

Address scaling to millions of users, ensuring low latency, and handling failures. Discuss trade-offs between consistency and availability, and between simple vs. complex algorithms.

Key Points to Mention

  • Integration with external APIs for flight schedules (e.g., FlightAware) and mapping services (e.g., Google Maps).
  • Real-time data processing using stream processing frameworks like Apache Kafka or Flink.
  • Geospatial indexing (e.g., using QuadTree or Geohash) for efficient location-based queries.
  • Caching strategies (e.g., Redis) to reduce latency for frequent queries like flight status.
  • Handling flight delays/cancellations with event-driven updates and fallback logic.
  • Security and privacy considerations for user location data (e.g., encryption, anonymization).

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