LinkedIn vs Atlassian: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the specific focus and patterns of each company's question bank can dramatically increase your efficiency. LinkedIn and Atlassian, while both major players, present distinct interview landscapes in terms of volume, difficulty, and core topic emphasis. A targeted approach, informed by their question breakdowns, is crucial for effective study.

Question Volume and Difficulty

The most immediate difference is the sheer scale of the question pools. LinkedIn's list, at 180 questions, is nearly three times the size of Atlassian's 62. This suggests a broader range of potential problems and a greater emphasis on encountering a wide variety of scenarios.

The difficulty distribution also reveals different hiring bar focuses. LinkedIn's breakdown (E26/M117/H37) shows a heavy concentration on Medium problems, which constitute about 65% of its list. This indicates that mastering medium-difficulty concepts—requiring a solid grasp of data structures and the ability to navigate non-trivial edge cases—is the key to passing their interviews. The presence of 37 Hard questions means you must also be prepared for complex optimization challenges, often involving advanced algorithms or multiple data structures.

Atlassian's list (E7/M43/H12) follows a similar pattern but is more concentrated. A striking 69% of its questions are Medium difficulty, an even higher proportion than LinkedIn's. This suggests Atlassian's technical screen is intensely focused on evaluating strong, practical problem-solving skills on standard algorithmic challenges, with less weight on the easiest or most extreme problems. The smaller overall volume means their question set is likely more curated and repeated, making pattern recognition highly valuable.

Topic Overlap

Both companies heavily test foundational data structures. Array, Hash Table, and String are top topics for both, confirming their universal importance in software engineering interviews.

# Example: A common pattern combining Hash Table and Array
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 []

# Usage
print(two_sum([2, 7, 11, 15], 9))  # Output: [0, 1]

The key divergence is LinkedIn's significant focus on Depth-First Search (DFS), a topic not highlighted in Atlassian's top four. This points to LinkedIn's greater emphasis on tree and graph traversal problems, which are common in systems dealing with hierarchical data (e.g., the social graph).

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

Conversely, Atlassian explicitly lists Sorting as a core topic, indicating a potential focus on problems involving ordering, merging, or scheduling data—highly relevant for their collaboration and project management tools.

Which to Prepare for First

Start with Atlassian. Its smaller, more concentrated question bank (62 questions, with 43 being Medium) provides a manageable and highly efficient study target. Mastering this list will solidify your core skills in Arrays, Hash Tables, Strings, and Sorting—which are also essential for LinkedIn. This foundation will build confidence and pattern recognition.

Then, move to LinkedIn. Use the broader 180-question list to expand your skills, particularly into Depth-First Search and other graph problems. Treat the larger volume as an opportunity to stress-test your foundational knowledge under more varied and complex scenarios, especially by practicing the additional Hard problems.

This sequential approach—mastering the focused, high-yield Atlassian set before tackling the expansive LinkedIn list—ensures you build from a solid core outward, maximizing the utility of your preparation time for both interview loops.

For targeted practice, visit the LinkedIn and Atlassian question lists: LinkedIn Interview Questions | Atlassian Interview Questions