Company preparationGuide 9 of 10
Uber · Company guide · Guide 9 of 10
Uber Data Engineering Interview Preparation
Prepare for Data Engineering interviews at Uber using its published 'How we hire' page and engineering interview posts, plus labelled practice questions on marketplace and real-time data.
On this page
Verified and attributed information
Uber's 'How we hire' page describes its general process, including conversations with the talent team and hiring manager, a technical interview for technical roles, possible role-specific exercises, team interviews and a decision step.
Source: Uber Careers: How we hire
Uber's engineering blog has published posts explaining parts of its engineering interview process, including coding interviews.
Source: Uber Engineering blog: Navigating our engineering interview process: coding
Sources
- Uber Careers: How we hire
- Uber Engineering blog: Navigating our engineering interview process: coding
Reported candidate questions
None yet. A question appears here only with a named, attributable source.
Representative practice questions
These are practice questions written for this guide. They are not claimed to be questions this company has asked.
Given trip events (requested, accepted, started, completed, cancelled), write SQL that computes the cancellation rate per city per hour.
Design a pipeline that computes driver supply and rider demand per area every minute. How do you handle late GPS events?
How would you deduplicate trip events that a mobile client may send more than once?
A real-time metric and the next day's batch report disagree. How do you find out which one is wrong?
Tell me about a time you had to make a decision quickly with incomplete data.
Preparation overview
Prepare in three areas: technical fundamentals, data system design, and behavioural stories. Marketplace businesses depend on fresh, correct event data, so expect emphasis on event-time processing and correctness.
Technology focus
Revise event-time windows, watermarks and deduplication alongside SQL fundamentals: batch vs streaming, Kafka fundamentals, window functions.
System-design focus
Practise the real-time analytics pipeline and Kafka ingestion case studies.
Behavioural preparation
Prepare stories about acting with incomplete information, owning an incident, and working across teams.
What this guide does not claim
This page does not state Uber’s number of rounds, interview questions, levelling or pay. Where Uber publishes information about its process, it is summarised above with a link; read the source for current details.