Urban Company Coding Questions 2025-2026 | Placement Interview Problems with Solutions

Urban Company’s coding round assesses DSA and problem-solving for marketplace and product engineering. The process typically includes an online assessment (e.g. 90 minutes) with 2–3 coding problems (medium focus) and technical interviews with DSA and system design (matching, booking, allocation). They test arrays, strings, graphs, DP, and hash-based solutions; they value optimal approach and marketplace-oriented thinking.

Urban Company Coding Round at a Glance

Format: OA with 2–3 coding problems; technical rounds with DSA and system design (marketplace). Difficulty: Medium. Topics: Arrays, strings, graphs, DP, matching/allocation. Focus: Optimal solutions, system design, and communication.

Coding Questions - Complete Problem Set

Practice coding interview questions with solutions.

Arrays & Strings

1. Two Sum

def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        if target - num in seen:
            return [seen[target - num], i]
        seen[num] = i
    return []

2. Valid Parentheses

def is_valid(s):
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}
    for char in s:
        if char in mapping:
            top = stack.pop() if stack else '#'
            if mapping[char] != top:
                return False
        else:
            stack.append(char)
    return not stack

3. Reverse String

def reverse_string(s):
    return s[::-1]

4. Palindrome Check

def is_palindrome(s):
    s = ''.join(c.lower() for c in s if c.isalnum())
    return s == s[::-1]

5. Merge Intervals

def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        if not merged or merged[-1][1] < interval[0]:
            merged.append(interval)
        else:
            merged[-1][1] = max(merged[-1][1], interval[1])
    return merged

6. Maximum Subarray

def max_subarray(nums):
    max_sum = current_sum = nums[0]
    for num in nums[1:]:
        current_sum = max(num, current_sum + num)
        max_sum = max(max_sum, current_sum)
    return max_sum

7. LRU Cache

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return -1
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)

Company-Specific Focus Areas

Urban Company often emphasizes arrays and strings (subarray, sliding window, validation), graphs (BFS/DFS, shortest path—relevant to matching and allocation), dynamic programming, hash maps, and system design (service matching, booking flows, real-time allocation) in technical rounds. Focus on optimal complexity and marketplace-oriented design; explaining trade-offs is important.

Related Resources

Interview Experience

Real interview experiences.

Preparation Guide

Complete preparation strategy.

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Practice these problems and focus on DSA and marketplace system design for Urban Company’s coding and technical rounds.

Last updated: February 2026