LinkedIn vs DoorDash: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus and expectations of each company can dramatically improve your efficiency. LinkedIn and DoorDash, while both testing core computer science fundamentals, present distinct profiles in their question selection, difficulty distribution, and topical emphasis. This comparison analyzes their question banks to help you tailor your preparation strategy.

Question Volume and Difficulty

The most immediate difference is the sheer volume of questions. LinkedIn's list of 180 questions is more than double DoorDash's 87. This suggests LinkedIn's interview process may draw from a broader pool of problems or that candidates report a wider variety of experiences.

The difficulty distribution also reveals different priorities:

• LinkedIn (E26/M117/H37): Medium difficulty questions dominate, comprising about 65% of the list. This indicates a strong focus on applying core algorithms to moderately complex scenarios. The significant number of Hard questions (over 20%) means you must be prepared for in-depth optimization challenges.
• DoorDash (E6/M51/H30): The emphasis is even more pronounced on Medium and Hard problems. Medium questions make up nearly 59% of the list, while Hard questions represent about 34%—a higher proportion than LinkedIn. The low number of Easy questions suggests DoorDash interviews quickly move to assessing problem-solving under constraints.

This data implies that while both require mastery of medium-level problems, DoorDash may place a slightly higher immediate emphasis on advanced problem-solving.

Topic Overlap

Both companies heavily test the same fundamental data structures. The top four topics for each are nearly identical: Array, String, Hash Table, and Depth-First Search. This overlap is excellent news for your preparation, as mastering these areas serves a dual purpose.

• Arrays & Strings: Expect questions on two-pointer techniques, sliding windows, and string manipulation.
• Hash Tables: Crucial for optimizing lookups and solving problems related to frequency counting, matching, and caching.
• Depth-First Search (DFS): Essential for tree and graph traversal, pathfinding, and backtracking problems.

The shared focus on DFS is particularly notable, signaling that both companies value recursive thinking and the ability to navigate complex data relationships. A strong grasp of graph theory and tree algorithms is non-negotiable.

# Example: DFS on a binary tree (common pattern)
def dfs(node, target):
    if not node:
        return False
    if node.val == target:
        return True
    # Recurse on left and right subtrees
    return dfs(node.left, target) or dfs(node.right, target)

Which to Prepare for First

Given the significant overlap in core topics, a unified foundational preparation is the most efficient approach. Start by solidifying your knowledge of Arrays, Strings, Hash Tables, and DFS/Graph algorithms. This core study will effectively prepare you for the majority of problems at both companies.

Once fundamentals are strong, tailor your final preparation based on your interview timeline:

• Prepare for DoorDash first if: Your interview is sooner or you want to tackle a list that is more concentrated on medium and hard challenges. The smaller, more difficult question bank allows for deep, focused practice.
• Prepare for LinkedIn first if: You have more time and want to cover a wider variety of problem patterns. The larger question bank will expose you to more scenarios, which also provides excellent general practice that will benefit you for DoorDash and other companies.

Ultimately, the shared technical foundation means preparing for one inherently prepares you for the other. Prioritize deep understanding of the common topics, then use company-specific lists for targeted practice and pattern recognition.

For further details, explore the question lists directly: LinkedIn Interview Questions and DoorDash Interview Questions.