Uber Python Interview Questions 2026
Published July 22, 2026 · Updated July 22, 2026
5 real Python interview questions asked at Uber. Upvoted by engineers who cleared the loop. Covers Technical, Scenario rounds.
5 questions
236 engineers asked
336 upvotes
Company: Uber
Technology: Python
All Python Questions Asked at Uber
Uber
Hard
Technical round
JavaPythonGo
Given a city map as a weighted graph, find the shortest travel time from a driver's current location to all potential passenger pickup points simultaneously. How does Dijkstra's multi-source variant solve this?
↑ 49 upvotes · 30 engineers asked this · SDE1
Uber
Medium
Technical round
JavaPython
Given a list of GPS coordinates representing a driver's path, compute the total distance travelled. Then find the longest contiguous segment where the driver was stationary (speed < 5 km/h). Discuss floating-point precision issues.
↑ 42 upvotes · 26 engineers asked this · SDE1
Uber
Hard
Scenario round
4–6 YearsPythonKafka
Your pipeline SLA requires data to be available in the warehouse by 6 AM. Yesterday it missed at 7:30 AM. Walk me through your incident response.
↑ 96 upvotes · 74 engineers asked this · Data Engineer
Uber
Hard
Scenario round
5–8 YearsPython
Your PySpark job has data skew on a join key (user_id) where 1% of users account for 60% of the data. How do you handle it?
↑ 117 upvotes · 91 engineers asked this · Data Engineer
Uber
Medium
Technical round
3-6 yearsPythonSystem Design
You need to find all available drivers within a 3km radius of a rider efficiently, with millions of driver location updates per second. What indexing structure would you use and why not a naive distance calculation over all drivers?
↑ 32 upvotes · 15 engineers asked this · Senior SDE
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Frequently asked questions
Given a city map as a weighted graph, find the shortest travel time from a driver's current location to all potential passenger pickup points simultaneously. How does Dijkstra's multi-source variant solve this?
Given a city map as a weighted graph, find the shortest travel time from a driver's current location to all potential passenger pickup points simultaneously. How does Dijkstra's multi-source variant solve this?
Given a list of GPS coordinates representing a driver's path, compute the total distance travelled. Then find the longest contiguous segment where the driver was stationary (speed < 5 km/h). Discuss floating-point precision issues.
Given a list of GPS coordinates representing a driver's path, compute the total distance travelled. Then find the longest contiguous segment where the driver was stationary (speed < 5 km/h). Discuss floating-point precision issues.
Your pipeline SLA requires data to be available in the warehouse by 6 AM. Yesterday it missed at 7:30 AM. Walk me through your incident response.
Your pipeline SLA requires data to be available in the warehouse by 6 AM. Yesterday it missed at 7:30 AM. Walk me through your incident response.
Your PySpark job has data skew on a join key (user_id) where 1% of users account for 60% of the data. How do you handle it?
Your PySpark job has data skew on a join key (user_id) where 1% of users account for 60% of the data. How do you handle it?
You need to find all available drivers within a 3km radius of a rider efficiently, with millions of driver location updates per second. What indexing structure would you use and why not a naive distance calculation over all drivers?
You need to find all available drivers within a 3km radius of a rider efficiently, with millions of driver location updates per second. What indexing structure would you use and why not a naive distance calculation over all drivers?