How to Crack eBay Coding Interviews in 2026

Getting an offer from eBay means proving you can solve the kind of algorithmic and system design problems they actually use. Their coding interview process typically involves one or two technical phone screens focusing on data structures and algorithms, followed by a virtual onsite with similar rounds, sometimes including a system design discussion for more senior roles. The questions are practical and test your fundamental problem-solving skills under pressure.

By the Numbers

Looking at the data from over 60 reported LeetCode questions, eBay's interview leans heavily toward medium difficulty. A full 63% (38 questions) are medium, with 20% easy (12 questions) and 17% hard (10 questions). This breakdown is crucial for your preparation strategy. It tells you that eBay is primarily testing for strong, all-around competency. You need to be highly proficient at solving medium problems within the interview timeframe. The hard questions are present, so you must be prepared to tackle complex challenges, but they are not the majority. The easy questions often serve as warm-ups or test your attention to detail on foundational concepts. Your goal is to master mediums so thoroughly that you can approach hards with a clear, structured methodology.

Top Topics to Focus On

The data shows a clear set of five core areas. You should prioritize these in your study plan.

• Array: This is the most frequent topic. Expect problems involving traversal, in-place manipulation, and subarray calculations. Practice techniques like two-pointers and sliding window until they are second nature.
• String: Often intertwined with array problems, string-specific manipulations, parsing, and comparison algorithms are common. Be comfortable with encoding, palindromes, and string builders.
• Hash Table: The go-to tool for achieving O(1) lookups to optimize your solutions. You will use it constantly for frequency counting, memoization, and tracking seen elements. Know its implementations inside and out.
• Sorting: Rarely the final answer but often a critical preprocessing step. Understand the trade-offs of different sorting algorithms and recognize when sorting can simplify a problem, especially in combination with two-pointers.
• Dynamic Programming: This appears consistently as a higher-order challenge. Focus on the core patterns—1D/2D DP, knapsack variants, and subsequence problems. Being able to derive the recurrence relation is more important than memorizing solutions.

Deep Dive: Array and Two-Pointers

Array problems often require efficient traversal without nested loops. The two-pointer technique is essential for this. One common pattern involves having one pointer at the start and another at the end, moving them inward based on a condition (e.g., finding a pair sum in a sorted array). Another pattern is the fast-slow pointer for detecting cycles or finding midpoints. The sliding window is a variant for contiguous subarrays with a condition on sum or unique characters.

# Two-Pointers: Find two numbers in a sorted array that sum to a target.
def two_sum_sorted(numbers, target):
    left, right = 0, len(numbers) - 1
    while left < right:
        current_sum = numbers[left] + numbers[right]
        if current_sum == target:
            return [left + 1, right + 1]  # 1-indexed
        elif current_sum < target:
            left += 1
        else:
            right -= 1
    return []

# Sliding Window: Maximum sum of any contiguous subarray of size k.
def max_sum_subarray(arr, k):
    if len(arr) < k:
        return 0
    window_sum = sum(arr[:k])
    max_sum = window_sum
    for i in range(k, len(arr)):
        window_sum = window_sum - arr[i - k] + arr[i]
        max_sum = max(max_sum, window_sum)
    return max_sum

Deep Dive: Hash Table Applications

Hash tables (dictionaries, maps) are used for O(1) average-time lookups. Beyond simple frequency counting, they are crucial for memoization in recursion (caching results of expensive function calls) and for implementing O(n) solutions to problems that would otherwise be O(n²), like the classic Two Sum problem. Understanding how to use a hash table to store "complements" or to act as a cache for previously seen states is key.

# Hash Table: Two Sum (unsorted array) - find indices of two numbers that add to target.
def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

# Hash Table: Frequency counter for anagrams.
def is_anagram(s, t):
    if len(s) != len(t):
        return False
    freq = {}
    for ch in s:
        freq[ch] = freq.get(ch, 0) + 1
    for ch in t:
        if ch not in freq or freq[ch] == 0:
            return False
        freq[ch] -= 1
    return True

Deep Dive: Dynamic Programming Patterns

Dynamic Programming problems at eBay often involve optimization or counting. The key is to identify the subproblem and the recurrence relation. For 1D DP, think of problems like climbing stairs or house robber. For 2D DP, think of edit distance or longest common subsequence. The knapsack pattern (0/1 or unbounded) is also common for problems involving selection with constraints. Always start by defining dp[i] or dp[i][j] and then determine how to build the solution from smaller subproblems.

# 1D DP: Climbing Stairs - number of ways to reach the top (1 or 2 steps at a time).
def climb_stairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2
    for i in range(3, n + 1):
        dp[i] = dp[i - 1] + dp[i - 2]
    return dp[n]
# Space-optimized version:
def climb_stairs_opt(n):
    if n <= 2:
        return n
    a, b = 1, 2
    for _ in range(3, n + 1):
        a, b = b, a + b
    return b

# 2D DP: Longest Common Subsequence (LCS).
def longest_common_subsequence(text1, text2):
    m, n = len(text1), len(text2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if text1[i - 1] == text2[j - 1]:
                dp[i][j] = dp[i - 1][j - 1] + 1
            else:
                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
    return dp[m][n]

Preparation Strategy

A focused 6-week plan is effective. This assumes you have a baseline knowledge of data structures.

• Weeks 1-2: Foundation & Core Topics. Dedicate this time exclusively to Array, String, and Hash Table. Solve 15-20 problems on each topic, mixing easy and medium. Do not just solve—analyze time/space complexity and practice verbalizing your thought process aloud.
• Weeks 3-4: Advanced Patterns & Practice. Move to Sorting-based problems and Dynamic Programming. For DP, start with classical problems (Fibonacci, coin change, longest common subsequence) before tackling variations. Begin mixing topics in your practice sessions.
• Week 5: Company-Specific & Mock Interviews. Solve all the easy and medium eBay-tagged questions on platforms like CodeJeet. Time yourself strictly. Schedule at least 3-4 mock interviews with a peer or mentor, simulating the real environment with video and a shared editor.
• Week 6: Review & Polish. Revisit problems you found difficult. Focus on your weak spots. Practice a few hard problems from the top topics to build stamina. In the final days, shift to conceptual review and rest.

Implementing the Strategy: A Sample Practice Problem

Let's apply the strategy to a classic eBay-style problem: "Find the maximum profit you can achieve from stock prices given an array where prices[i] is the price on day i. You may complete as many transactions as you like (buy one and sell one share multiple times)." This is a greedy/array problem.

Step 1: Understand and Communicate. The key is realizing that you can capture every upward slope (price increase from one day to the next). You don't need to find peaks and valleys separately.

Step 2: Discuss Trade-offs. A brute-force approach would try every combination of buy/sell days, leading to O(n²) or exponential time. The optimal greedy approach is O(n) time and O(1) space.

Step 3: Code the Solution.

def max_profit(prices):
    total_profit = 0
    for i in range(1, len(prices)):
        if prices[i] > prices[i - 1]:
            total_profit += prices[i] - prices[i - 1]
    return total_profit

Step 4: Test Your Code. Walk through an example: prices = [7,1,5,3,6,4]. The profit is (5-1) + (6-3) = 4 + 3 = 7. Edge cases: empty array (return 0), descending prices (return 0), single price (return 0).

Key Tips

1. Optimize Your Medium-Solving Speed. Your primary benchmark is the ability to correctly solve a medium problem in 25-30 minutes, including explanation and complexity analysis. Practice under this constraint daily. Use a timer for every practice problem.
2. Communicate Before You Code. eBay values collaborative engineers. Narrate your thought process, discuss trade-offs between approaches, and confirm your understanding of the problem before writing a single line of code. Silence is your enemy. Start by restating the problem in your own words and asking clarifying questions about input constraints and edge cases.
3. Consider Trade-Offs Explicitly. When presenting your solution, explicitly state, "This approach uses O(n) extra space for the hash map to achieve O(n) time complexity. The alternative would be O(n^2) time with constant space." This shows depth of understanding. Be prepared to discuss when you might choose one trade-off over another (e.g., memory constraints).
4. Test Your Own Code. After writing your solution, don't wait for the interviewer to ask. Walk through a small test case, including edge cases (empty input, single element, large values, negative numbers if applicable). Verbally step through the logic to prove it works. Mention how you would write unit tests.
5. Prepare a Strong "Tell Me About Yourself." The interview often starts with this. Craft a concise, 60-90 second narrative that connects your background directly to the role and eBay's marketplace or payments domains. For example, "I'm a backend engineer with five years of experience building scalable data pipelines. At my last company, I optimized a recommendation system, which directly relates to eBay's challenge of personalizing the marketplace for millions of users. I'm excited to apply my problem-solving skills to eBay's unique technical challenges."

Success in an eBay interview comes from methodical preparation on their favored topics and consistent practice communicating your solutions. Focus on the medium-difficulty core, and you'll be well-positioned to handle the challenges they present.

Browse all eBay questions on CodeJeet