Goldman Sachs vs Atlassian: Interview Question Comparison

When preparing for technical interviews at top companies, understanding the specific focus and expectations of each can dramatically improve your efficiency. Goldman Sachs and Atlassian, while both prestigious, present distinct interview landscapes in terms of volume, difficulty, and topic emphasis. This comparison breaks down their technical question profiles to help you tailor your preparation strategy.

Question Volume and Difficulty

The sheer number of cataloged questions is the most immediate difference. Goldman Sachs has a significantly larger pool of 270 questions, compared to Atlassian's 62 questions. This suggests that Goldman Sachs interviews pull from a much broader set of problems, making it harder to predict what you might see. The difficulty distribution also varies:

• Goldman Sachs (E51/M171/H48): The majority of questions are Medium (171), with a substantial number of Hard (48). This indicates a strong emphasis on complex problem-solving, often requiring optimized solutions and handling edge cases under pressure.
• Atlassian (E7/M43/H12): The focus is also on Medium-difficulty problems (43), but with far fewer Hard questions (12). The overall smaller pool and lower proportion of Hard problems suggest a slightly more predictable interview process centered on core algorithmic competence.

Topic Overlap

Both companies heavily test fundamental data structures. The top four topics are identical, though in a different order of priority:

• Goldman Sachs: Array, String, Hash Table, Dynamic Programming
• Atlassian: Array, Hash Table, String, Sorting

Array, String, and Hash Table questions form the common core. You can expect problems involving manipulation, traversal, and lookup using these structures at both companies. The key divergence is the fourth topic.

Dynamic Programming (DP) is a major focus for Goldman Sachs, aligning with its higher count of Hard problems. DP questions test advanced problem decomposition and optimization. Sorting is the distinct priority for Atlassian, pointing to an emphasis on algorithms that efficiently organize data, which is crucial for many backend and system design scenarios in their collaboration tools.

Here is a typical problem that could appear at either company, solved using a Hash Table:

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 []

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

Which to Prepare for First

Your preparation order should be guided by the breadth and depth of the question banks.

Start with Atlassian's core. Its smaller, more concentrated question set on Array, Hash Table, String, and Sorting provides a solid and manageable foundation. Mastering these will build the essential skills needed for most interviews. Ensure you are comfortable with all major sorting algorithms and their trade-offs.

Then, expand to Goldman Sachs. After mastering the common core, layer on the additional complexity required for Goldman Sachs. This means dedicated practice in Dynamic Programming (e.g., knapsack, longest common subsequence, and pathfinding problems) and tackling a wider variety of Medium and Hard problems to build stamina for their larger question pool.

In short, Atlassian's profile offers a focused starting point. Achieving fluency there will cover a significant portion of the foundational material for Goldman Sachs, which then requires expanding into more advanced topics and volume practice.

For detailed question lists, visit the Goldman Sachs and Atlassian pages on CodeJeet.