Uber
Ride-hailing and delivery platform with massive real-time data challenges.
4 Rounds
~21 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Cloud Engineer
•
System Design
•
hard
Design a multi-region, active-active deployment architecture for Uber's ride-matching service to ensure zero downtime during a regional cloud outage.
#High Availability
#Multi-region
#Disaster Recovery
#Cloud Architecture
Cloud Engineer
•
System Design
•
hard
Design a disaster recovery strategy for Uber's payment processing gateway, ensuring an RPO (Recovery Point Objective) of near zero and an RTO (Recovery Time Objective) of under 5 minutes.
#Disaster Recovery
#Databases
#Networking
#Payments
Cloud Engineer
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System Design
•
hard
Design an observability and metrics pipeline capable of ingesting millions of data points per second from Uber's global fleet of microservices.
#Observability
#Distributed Systems
#Kafka
#Data Pipelines
Cloud Engineer
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System Design
•
hard
Design an Infrastructure as Code (IaC) provisioning system using Terraform that allows hundreds of Uber engineering teams to deploy resources safely without state conflicts.
#Terraform
#CI/CD
#Infrastructure as Code
#Security
Data Engineer
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System Design
•
hard
Design a real-time data pipeline to calculate surge pricing multipliers. The system needs to aggregate ride requests and available drivers per H3 hexagon (resolution 9) every 30 seconds.
#Stream Processing
#Kafka
#Flink
#Geospatial Data
#State Management
Data Engineer
•
System Design
•
hard
Design the data architecture and ETL pipelines to generate the daily payout reports for Uber Eats restaurants. Consider that restaurants can have different timezone cutoffs and complex, tiered commission structures.
#ETL/ELT
#Batch Processing
#Airflow
#Data Warehousing
Data Engineer
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System Design
•
hard
Design a system to ingest and process telemetry data (GPS, speed, heading) from millions of active drivers in real-time to power the live map view and feed ETA machine learning models.
#High Throughput Ingestion
#Kafka
#Data Partitioning
#NoSQL
Data Scientist
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System Design
•
hard
Design the surge pricing algorithm. What are the key inputs, what is the objective function, and how do you balance rider conversion with driver supply?
#Dynamic Pricing
#Marketplace Equilibrium
#Optimization
Data Scientist
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System Design
•
hard
How would you design a matching algorithm for UberX Share (formerly UberPool)? What trade-offs do you need to consider between match rate, detour time, and rider experience?
#Graph Algorithms
#Optimization
#Heuristics
#Marketplace Routing
Machine Learning Engineer
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Coding
•
medium
Implement a rate limiter for the Uber API to prevent abuse. The rate limiter should allow a maximum of N requests per user per minute.
#Data Structures
#Concurrency
#Sliding Window
Product Manager
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System Design
•
hard
Design the high-level architecture for Uber's surge pricing calculation engine. How do you handle real-time demand and supply spikes?
#Scalability
#Real-time Processing
#Microservices
Product Manager
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System Design
•
hard
Design a system to track driver locations in real-time. How would you balance the need for high-frequency location updates with the constraint of driver mobile battery drain?
#Mobile Architecture
#Geospatial Data
#Optimization
Product Manager
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System Design
•
medium
Design the backend architecture for Uber Eats restaurant menu ingestion. How do you handle thousands of restaurants updating their menus, prices, and item availability concurrently?
#Data Ingestion
#Consistency
#API Design
Software Engineer
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System Design
•
hard
Design the backend architecture for Uber Eats, focusing specifically on the order state machine and how to handle real-time updates to the customer, restaurant, and courier.
#Microservices
#WebSockets
#State Machines
#Event-Driven Architecture
#Pub/Sub
Software Engineer
•
System Design
•
hard
Design a distributed logging and monitoring system for Uber's microservices that can ingest millions of logs per second and provide real-time alerting.
#Elasticsearch
#Kafka
#Log Aggregation
#Observability
#Data Pipelines
Software Engineer
•
System Design
•
hard
Design Uber's dynamic pricing (surge pricing) system. How would you handle the data ingestion of millions of location pings and calculate the surge multiplier in real-time?
#Stream Processing
#Kafka
#Apache Flink
#Distributed Caching
Software Engineer
•
System Design
•
hard
Design a geospatial index to quickly find the nearest available Uber drivers for a rider requesting a trip in a high-density city like New York.
#QuadTrees
#Geohashing
#Spatial Databases
#Scalability
#Redis
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.