Greedy Questions at Atlassian: What to Expect

Greedy algorithms are a small but critical part of Atlassian's technical interview repertoire, representing about 11% of their tagged problems. While not the most frequent, they test a candidate's ability to make a series of locally optimal choices to arrive at a globally optimal solution—a skill directly applicable to optimizing resource allocation, scheduling, and feature rollout decisions in large-scale systems like Jira or Confluence. Mastering these questions demonstrates you can think pragmatically about efficiency and trade-offs, a valued trait for engineers building and maintaining their platforms.

What to Expect — Types of Problems

Atlassian's greedy questions typically fall into predictable categories that align with real-world engineering scenarios. You can expect problems centered on interval scheduling (e.g., meeting room allocation, task scheduling), array manipulation and sorting (e.g., maximizing profit, minimizing wait time), and string manipulation (e.g., reorganizing strings, minimal edits). The problems are often framed in a business context, such as optimizing server loads, arranging data migrations, or managing continuous deployment pipelines. The key is to recognize the greedy property: that a greedy choice at each step leads to an optimal solution, often requiring the data to be sorted in a specific way first.

How to Prepare — Study Tips with One Code Example

Start by solidifying the core greedy patterns: activity selection, coin change (canonical systems), and minimum spanning tree concepts. For each problem, practice the two-step approach: first, prove or convince yourself that a greedy choice is safe (usually by exchange argument), then implement the sorting and iteration. Always validate with edge cases—empty inputs, single elements, and already-sorted data.

A fundamental pattern is the "minimum number of meetings/activities" problem, often asked as scheduling the maximum number of non-overlapping intervals. The greedy choice is to always pick the activity that ends the earliest.

def max_meetings(intervals):
    # Sort by end time
    intervals.sort(key=lambda x: x[1])
    count = 0
    last_end = -float('inf')

    for start, end in intervals:
        if start >= last_end:
            count += 1
            last_end = end
    return count

Recommended Practice Order

Tackle problems in this sequence to build competency: begin with classic leetcode easy problems like "Assign Cookies" or "Lemonade Change" to internalize the greedy decision process. Move to medium-difficulty interval problems (like the example above) and "Jump Game" variants. Finally, attempt Atlassian’s specific tagged questions, which often combine greedy logic with light data structure use. Timebox your practice: spend no more than 10-15 minutes identifying the greedy strategy before checking the solution if stuck. The goal is pattern recognition, not brute-force derivation.

Practice Greedy at Atlassian