Preparing for a coding interview at Atlassian requires a focused approach on data structures, algorithms, and demonstrating your problem-solving prowess. Atlassian is renowned for its emphasis on clean, production-ready code, adaptability to new requirements, and a deep understanding of conceptual thinking. Unlike some companies that might prioritize obscure algorithmic tricks, Atlassian coding questions often resemble medium-level LeetCode problems designed to assess your fundamental computer science knowledge and ability to reason through complex challenges. Success hinges not just on finding a solution, but on explaining your thought process, discussing trade-offs, and showing how your solution could scale. This guide provides an in-depth look at what to expect and offers strategies for tackling 30 frequently encountered coding questions.
What Are Atlassian Coding Questions?
Atlassian coding questions are primarily technical challenges designed to evaluate a candidate's proficiency in core computer science concepts, specifically data structures and algorithms. These questions typically mirror medium-difficulty problems found on platforms like LeetCode. Interviewers at Atlassian look beyond just a correct answer, assessing the candidate's approach to problem-solving, the clarity and efficiency of their code, and their ability to adapt to new constraints or requirements during the interview. The focus is on demonstrating a structured thinking process, making reasoned decisions about different approaches, and considering the scalability of your proposed solutions. Common topics include string manipulation, arrays, sorting, searching, graph traversal, dynamic programming, and various data structure implementations like priority queues and tries.
Why Do Interviewers Ask Atlassian Coding Questions?
Interviewers ask Atlassian coding questions to gauge a candidate's foundational technical skills and their practical application in real-world scenarios. These questions reveal how you approach complex problems, from breaking them down into manageable parts to designing an efficient and maintainable solution. Atlassian values engineers who can write clean, production-quality code, articulate their thought process clearly, and justify design choices. Coding questions help assess adaptability, as interviewers often introduce new requirements mid-challenge to see how you respond to change. They also test your ability to use fundamental data structures and algorithms effectively, your resourcefulness, and your capacity to think about the performance and scalability of your code. Ultimately, it's about understanding your engineering mindset and potential contribution to Atlassian's collaborative and innovative environment.
Implement LRU Cache
Merge Intervals
Longest Substring Without Repeating Characters
Top K Frequent Elements
Valid Parentheses
Group Anagrams
Search in Rotated Sorted Array
Number of Islands (DFS)
Clone Graph
Course Schedule (Graph cycle detection)
Implement Trie with Insert and Search
Meeting Rooms (Interval scheduling)
Kth Largest Element in an Array
Design Circular Queue
Sliding Window Maximum
Design Snake Game
Decode String (String parsing)
Serialize and Deserialize Binary Tree
Find Median from Data Stream
Word Ladder (BFS)
Palindrome Partitioning
Longest Increasing Subsequence
Rank Teams by Votes
Coin Change (DP)
Evaluate Reverse Polish Notation
Subsets and Permutations
Insert Interval
Word Search (Backtracking)
Implement Autocomplete System
Implement Min Stack
Preview List
1. Implement LRU Cache
Why you might get asked this:
This question assesses your understanding of data structures (hash maps and doubly linked lists) and how to combine them for an efficient caching mechanism, crucial for system design.
How to answer:
Use a hash map for O(1) lookups and a doubly linked list for maintaining the order of usage and eviction policies. Handle put and get operations efficiently.
Example answer:
The core idea is to use a HashMap to store key-value pairs for O(1) access and a DoublyLinkedList to maintain the least recently used order. When an item is accessed, move it to the front of the list. If the cache is full upon insertion, remove the item from the tail.
2. Merge Intervals
Why you might get asked this:
This tests your ability to handle array/list manipulation, sorting, and conditional logic, common in scheduling or data consolidation problems.
How to answer:
Sort intervals by their start times. Iterate through the sorted intervals, merging overlapping ones by extending the end time of the current merged interval.
Example answer:
First, sort the intervals by their start times. Initialize a result list with the first interval. Then, iterate through the remaining intervals. If the current interval overlaps with the last one in the result list, merge them. Otherwise, add the current interval to the result.
3. Longest Substring Without Repeating Characters
Why you might get asked this:
A classic string manipulation problem, it evaluates your grasp of sliding window techniques and character set tracking, vital for efficient string processing.
How to answer:
Employ a sliding window approach with a hash set or map to keep track of characters within the current window and efficiently check for duplicates.
Example answer:
Use a sliding window defined by two pointers, left and right. Maintain a HashSet to store characters in the current window. Expand right. If a duplicate is found, shrink the window from left until the duplicate is removed, updating the maximum length.
4. Top K Frequent Elements
Why you might get asked this:
This problem assesses your knowledge of hash maps for frequency counting and priority queues (or sorting) for selecting top elements efficiently.
How to answer:
Count frequencies using a hash map. Then, use a min-priority queue to keep track of the k most frequent elements, or sort the elements by frequency.
Example answer:
First, count the frequency of each number using a HashMap. Then, use a Min-PriorityQueue to store k (frequency, number) pairs. Iterate through the map entries, adding them to the queue. If the queue size exceeds k, poll the smallest element. Finally, extract elements from the queue.
5. Valid Parentheses
Why you might get asked this:
A fundamental problem testing stack data structure usage for pattern matching and balanced symbol checking, relevant for parsing and syntax validation.
How to answer:
Use a stack. Push opening parentheses onto the stack. When a closing parenthesis appears, check if it matches the top of the stack and pop.
Example answer:
Initialize an empty stack. Iterate through the string. If an opening bracket is encountered, push it onto the stack. If a closing bracket is found, check if the stack is empty or if its top matches the corresponding opening bracket. Pop if matched, otherwise return false. Finally, ensure the stack is empty.
6. Group Anagrams
Why you might get asked this:
Tests your ability to categorize strings based on a computed canonical form (sorted string or character counts), showcasing dictionary/map usage.
How to answer:
For each word, create a canonical key (e.g., sort the characters or count character frequencies). Store words in a hash map where keys are these canonical forms.
Example answer:
Iterate through the list of words. For each word, sort its characters to form a "key" (e.g., "eat" becomes "aet"). Use a HashMap where the key is this sorted string and the value is a list of original words that share this sorted key. Finally, return all the lists of words from the map.
7. Search in Rotated Sorted Array
Why you might get asked this:
This problem challenges your understanding of binary search in a modified scenario, requiring careful handling of pivot points and search boundaries.
How to answer:
Apply a modified binary search. Determine which half of the array is sorted and check if the target lies within that sorted range. Adjust search boundaries accordingly.
Example answer:
Perform a binary search. In each step, determine which half of the array (left or right of mid) is sorted. If the target is in the sorted half, narrow the search to that half. Otherwise, search the unsorted half. Continue until the target is found or boundaries cross.
8. Number of Islands (DFS)
Why you might get asked this:
A classic graph traversal problem that assesses your recursion skills and understanding of Depth-First Search (DFS) for connected components.
How to answer:
Iterate through the grid. When an unvisited '1' (land) is found, increment island count and start a DFS from that point to mark all connected land cells as visited.
Example answer:
Iterate through each cell of the grid. If a cell contains '1' (land), increment the island count and then perform a Depth-First Search (DFS) starting from that cell. The DFS function should recursively visit all adjacent '1's (up, down, left, right) and mark them as visited ('0') to avoid recounting.
9. Clone Graph
Why you might get asked this:
Evaluates your ability to traverse graphs, manage visited nodes (to prevent cycles), and correctly create copies of nodes and their connections.
How to answer:
Use BFS or DFS to traverse the graph. Maintain a hash map to store mappings from original nodes to their cloned counterparts to avoid infinite loops and re-cloning.
Example answer:
Use a HashMap to store the mapping from original nodes to their cloned copies, along with a Queue for BFS traversal (or use recursion for DFS). Start traversal from the given node, cloning each node and its neighbors, populating the map to ensure no node is cloned twice and correctly setting up neighbor references.
10. Course Schedule (Graph cycle detection)
Why you might get asked this:
Tests your understanding of directed graphs, topological sorting, and cycle detection, critical for dependency management or build systems.
How to answer:
Model courses and prerequisites as a directed graph. Use Kahn's algorithm (BFS with in-degrees) or DFS with visited states to detect cycles.
Example answer:
Represent courses as a directed graph where an edge from A to B means A is a prerequisite for B. Use topological sort (e.g., Kahn's algorithm with in-degrees, or DFS with three states: unvisited, visiting, visited) to determine if a cycle exists. If a cycle is detected, courses cannot be finished.
11. Implement Trie with Insert and Search
Why you might get asked this:
Assesses your knowledge of tree-based data structures for efficient string prefix searching, common in autocomplete or spell checker systems.
How to answer:
Implement a Trie data structure using nodes where each node contains children pointers (or a map) and a flag indicating end of word.
Example answer:
Design a TrieNode class with an array or map of children (representing the next characters) and a boolean flag isEndOfWord. Implement insert(word) by traversing and creating nodes as needed. Implement search(word) by traversing and checking for the isEndOfWord flag at the end.
12. Meeting Rooms (Interval scheduling)
Why you might get asked this:
A variation of interval problems, requiring sorting and a greedy approach to check for overlaps, pertinent to resource allocation.
How to answer:
Sort intervals by start times. Iterate and check if any current interval's start time is less than the previous interval's end time. If so, there's an overlap.
Example answer:
Sort the meeting intervals based on their start times. Then, iterate through the sorted intervals, checking if the current meeting's start time is earlier than the previous meeting's end time. If this condition is met at any point, it means an overlap exists, and the person cannot attend all meetings.
13. Kth Largest Element in an Array
Why you might get asked this:
Tests your understanding of selection algorithms like Quickselect (partitioning) or using a min-priority queue for efficient element retrieval.
How to answer:
Use a min-priority queue of size k. Iterate through the array; add elements to the queue. If size exceeds k, remove smallest. Alternatively, use Quickselect.
Example answer:
One effective approach is to use a min-priority queue (min-heap) of size k. Iterate through the array, adding each element to the heap. If the heap's size exceeds k, remove the smallest element (heap's root). After processing all elements, the heap's root will be the Kth largest element.
14. Design Circular Queue
Why you might get asked this:
Evaluates your understanding of queue principles and array manipulation to create a fixed-size, reusable data structure, common in buffer management.
How to answer:
Use an array and two pointers (front and rear) to manage elements, wrapping around the array using the modulo operator to simulate circularity.
Example answer:
Implement using a fixed-size array and two pointers, front and rear, to track the head and tail. Use the modulo operator (% capacity) to handle circularity when incrementing pointers. Maintain a count or a size variable to distinguish between full and empty states.
15. Sliding Window Maximum
Why you might get asked this:
A challenging sliding window problem requiring a deque (double-ended queue) to efficiently track the maximum element within a moving window.
How to answer:
Use a deque to store indices of elements. Maintain the deque such that elements are in decreasing order, and only valid window indices remain.
Example answer:
Use a Deque to store indices of elements in the current window. Maintain the Deque in a way that the elements at these indices are in decreasing order. As the window slides, remove indices from the front if they are out of bounds and from the back if new elements are larger, then add the current index.
16. Design Snake Game
Why you might get asked this:
This problem combines elements of queue/list management (for the snake's body), set/hash map usage (for quick lookup of body segments and food), and coordinate system logic.
How to answer:
Use a Deque or LinkedList to represent the snake's body coordinates, allowing efficient head/tail operations. A HashSet can track occupied cells for collision detection.
Example answer:
Implement using a Deque (e.g., LinkedList) to store the snake's body coordinates, where the head is at one end and the tail at the other. Use a HashSet to quickly check for snake body collisions. Track food positions and update score/snake length accordingly, ensuring the snake moves correctly and handles game-over conditions.
17. Decode String (String parsing)
Why you might get asked this:
Tests recursive parsing or stack-based string manipulation, common in compiler design or data serialization/deserialization.
How to answer:
Use two stacks: one for numbers (repetition counts) and one for strings. Process characters, handling digits, brackets, and letters to build the decoded string.
Example answer:
Use two stacks: one for numbers (repetition counts) and one for strings (intermediate decoded parts). When a digit is seen, build the number. When '[' is seen, push current string and number onto stacks. When ']' is seen, pop and repeat the string. Build the final string iteratively.
18. Serialize and Deserialize Binary Tree
Why you might get asked this:
A classic tree problem requiring careful traversal (BFS or DFS) to flatten a tree into a string and reconstruct it, essential for data storage or transmission.
How to answer:
Use BFS (level-order traversal) or DFS (pre-order traversal) to serialize the tree into a string, using a special marker for null nodes. For deserialization, reconstruct using the same traversal order.
Example answer:
For serialization, perform a pre-order traversal (DFS) or level-order traversal (BFS). Append node values to a string, using a special marker (e.g., "N" or "#") for null children to preserve tree structure. For deserialization, parse the string and reconstruct the tree using the same traversal logic, queueing values for BFS or recursively building for DFS.
19. Find Median from Data Stream
Why you might get asked this:
This problem assesses your ability to maintain sorted data efficiently using two heaps (min-heap and max-heap) for finding medians in real-time.
How to answer:
Use two heaps: a max-heap for the smaller half of numbers and a min-heap for the larger half. Balance the heaps so their sizes differ by at most one.
Example answer:
Maintain two heaps: a max-heap for the lower half of numbers and a min-heap for the upper half. Always insert new numbers into the max-heap first, then move the largest from the max-heap to the min-heap. Rebalance if heap sizes differ by more than one. The median is then derived from the tops of the heaps.
20. Word Ladder (BFS)
Why you might get asked this:
A graph traversal problem where words are nodes and connections are single-character differences, typically solved with Breadth-First Search (BFS).
How to answer:
Model as a graph where words are nodes and an edge exists if words differ by one character. Use BFS to find the shortest path, tracking visited words.
Example answer:
Treat words as nodes in a graph. An edge exists between two words if they differ by exactly one character. Use Breadth-First Search (BFS) starting from beginWord to find the shortest path to endWord. A queue stores words to visit, and a HashSet tracks visited words to prevent cycles.
21. Palindrome Partitioning
Why you might get asked this:
Tests dynamic programming or backtracking with palindrome checks, useful for text processing and pattern recognition.
How to answer:
Use backtracking. At each step, iterate through possible substrings starting from the current position. If a substring is a palindrome, recursively solve for the remainder.
Example answer:
This is a classic backtracking problem. Define a recursive function that takes the current starting index. Iterate from this index to the end of the string, checking if each substring is a palindrome. If it is, add it to the current partition and recursively call the function for the remaining string. Backtrack by removing the substring.
22. Longest Increasing Subsequence
Why you might get asked this:
A dynamic programming problem requiring careful state definition and optimization, foundational for sequence analysis.
How to answer:
Use dynamic programming where dp[i] stores the length of the longest increasing subsequence ending at index i. Alternatively, a more optimized approach uses binary search.
Example answer:
A dynamic programming approach involves dp[i] storing the length of the longest increasing subsequence ending at nums[i]. Iterate through nums, and for each nums[i], iterate j from 0 to i-1. If nums[i] > nums[j], then dp[i] = max(dp[i], dp[j] + 1). The maximum value in dp is the answer.
23. Rank Teams by Votes
Why you might get asked this:
A sorting problem that requires custom comparison logic based on multiple criteria (ranks at each position), highlighting stable sorting.
How to answer:
Store vote counts for each team at each rank position. Then, sort teams based on a custom comparator that iterates through rank positions.
Example answer:
For each team, create an array representing its vote counts at each rank position (e.g., counts[team][rank_index]). Sort the teams using a custom comparator. This comparator will compare two teams by iterating through their rank counts from left to right. If counts are equal, proceed to the next rank. If all counts are equal, sort lexicographically.
24. Coin Change (DP)
Why you might get asked this:
A classic dynamic programming problem that assesses your ability to build up solutions from subproblems, critical for optimization problems.
How to answer:
Use dynamic programming. dp[i] represents the minimum number of coins to make amount i. Iterate through amounts, and for each amount, iterate through coin denominations.
Example answer:
Use dynamic programming. Create a dp array where dp[i] is the minimum number of coins to make amount i. Initialize dp[0]=0 and others to infinity. Iterate from amount 1 to target amount. For each amount, iterate through available coins. dp[i] = min(dp[i], dp[i - coin] + 1).
25. Evaluate Reverse Polish Notation
Why you might get asked this:
Tests stack data structure usage for arithmetic expression evaluation, a core concept in compiler design and calculators.
How to answer:
Use a stack. Push numbers onto the stack. When an operator is encountered, pop the necessary operands, perform the operation, and push the result back.
Example answer:
Initialize an empty stack. Iterate through the RPN tokens. If a token is a number, push it onto the stack. If it's an operator (+, -, *, /), pop the top two operands from the stack, perform the operation, and push the result back onto the stack. The final result will be the only element left on the stack.
26. Subsets and Permutations
Why you might get asked this:
Classic backtracking problems demonstrating combinatorial thinking and recursive exploration of all possible combinations or orderings.
How to answer:
Use a recursive backtracking approach. For subsets, at each element, decide to include it or not. For permutations, swap elements or use a visited array.
Example answer:
For subsets, use backtracking: define a recursive function that explores two paths for each element – either include it in the current subset or don't. For permutations, use backtracking by swapping elements or using a boolean array to track visited elements, ensuring each element is used exactly once in each permutation.
27. Insert Interval
Why you might get asked this:
Similar to merge intervals, this tests interval manipulation, requiring careful handling of insertion, merging, and maintaining sorted order.
How to answer:
Iterate through existing intervals. Add intervals that come before newInterval. Merge newInterval with overlapping ones. Add remaining intervals after the merge.
Example answer:
Iterate through the existing intervals. First, add all intervals that end before newInterval starts. Then, merge newInterval with any overlapping intervals by updating its start and end. Finally, add the merged newInterval and any remaining intervals that start after it.
28. Word Search (Backtracking)
Why you might get asked this:
A grid-based backtracking problem assessing recursive search (DFS) and state management (marking visited cells) for finding paths.
How to answer:
Use DFS (backtracking). For each cell, try to match the first character. If matched, recursively search adjacent cells for the next character, marking visited cells.
Example answer:
Iterate through each cell in the grid. If a cell matches the first character of the word, start a recursive Depth-First Search (DFS) from that cell. The DFS function will explore adjacent cells, marking current cells as visited (e.g., changing 'A' to '#') to avoid reuse, and backtracking by restoring the cell value.
29. Implement Autocomplete System
Why you might get asked this:
This problem often involves a Trie combined with priority queues or sorting to provide ranked suggestions, assessing complex data structure integration.
How to answer:
Combine a Trie to store sentences and their frequencies with a mechanism (like a min-priority queue) to retrieve top k suggestions for a given prefix.
Example answer:
Implement a Trie where each node stores a map of children characters and potentially a list of sentences that pass through it, along with their frequencies. When a search prefix is given, traverse the Trie. From the node representing the end of the prefix, collect all sentences and their frequencies using DFS and then sort them by frequency (and lexicographically) to get the top k.
30. Implement Min Stack
Why you might get asked this:
Tests your ability to extend basic data structures. It requires maintaining the minimum element in O(1) time complexity for push, pop, and top operations.
How to answer:
Use two stacks: one for regular elements and another to keep track of the minimums. When pushing, push to both if new element is smaller or equal.
Example answer:
Use two stacks: one mainStack for all elements and another minStack to store the minimum value encountered so far at each step. When pushing, push to mainStack. If the new element is less than or equal to minStack.peek(), push it to minStack. When popping, if mainStack.peek() equals minStack.peek(), pop from minStack too.
Other Tips to Prepare for an Atlassian Interview
Effective preparation for an Atlassian coding interview goes beyond just solving problems; it’s about refining your entire approach. As renowned computer scientist Donald Knuth once said, "Science is what we understand well enough to explain to a computer. Art is everything else we do." Your ability to explain your logic clearly is as crucial as the code itself. Practice medium-level LeetCode problems consistently, focusing on clean, readable code. Atlassian values code that's production-ready, so pay attention to variable naming, modularity, and error handling.
Be prepared to discuss the time and space complexity of your solutions and consider how they would scale with larger inputs. Interviewers often introduce follow-up questions or new constraints to assess your adaptability; embrace these as opportunities to demonstrate your flexible problem-solving skills. Using your preferred programming language can help you write more fluently and articulate your ideas better. For structured practice and AI-powered feedback on your verbal responses and technical explanations, consider using tools like Verve AI Interview Copilot. Verve AI Interview Copilot offers real-time coaching and helps refine your communication. You can explore more at https://vervecopilot.com. Remember, "The only way to do great work is to love what you do," and preparing diligently will build your confidence and showcase your passion for engineering. Verve AI Interview Copilot can be a valuable partner in this journey.
Frequently Asked Questions
Q1: What programming languages are allowed in Atlassian coding interviews?
A1: Atlassian typically allows you to use any major programming language you are proficient in, such as Python, Java, C++, or JavaScript.
Q2: Should I memorize solutions to all LeetCode problems?
A2: No, focus on understanding common algorithms and data structures, and the problem-solving patterns. Memorization is less effective than genuine comprehension.
Q3: How much time should I allocate for preparation?
A3: This varies by individual, but consistent practice for several weeks to a few months is common, focusing on quality over quantity in problem-solving.
Q4: Is it okay to ask clarifying questions during the interview?
A4: Absolutely, asking clarifying questions is highly encouraged. It demonstrates critical thinking and ensures you understand the problem requirements fully.
Q5: What if I get stuck on a coding problem?
A5: Communicate your thought process. Talk through your ideas, even if incomplete. Interviewers want to see how you approach challenges and your problem-solving methodology.
