Atlassian·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Atlassian software engineering interview that went pretty deep into tree problems and system design. The question had a lot of layers to it and I wasn't fully prepared for how far they'd push on the operational side of things.

Questions Asked (1)

Given an organizational hierarchy represented as a tree, find the lowest common ancestor for two or more employees. You're also expected to write unit tests, explain your tree representation, and discuss how you'd handle dynamic user/group additions and deletions in a thread-safe way.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The LCA part I was fine with.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the tree representation and constraints, then propose an efficient LCA algorithm (e.g., binary lifting or Euler tour + RMQ) that handles multiple nodes. For dynamic updates, discuss thread-safe data structures like concurrent maps or read-write locks, and outline a unit testing strategy covering edge cases.

Pro tip: Mention that for multiple nodes, the LCA can be computed pairwise or by finding the deepest node that is an ancestor of all, and highlight the trade-offs between preprocessing time and query time. Also, emphasize the importance of immutable snapshots or versioning for thread-safe reads during updates.

1. Clarify Requirements and Constraints

Ask about the tree size, expected number of queries, update frequency, and concurrency requirements. Confirm whether the hierarchy is static or dynamic, and if multiple LCA queries are for the same tree.

2. Choose Tree Representation and LCA Algorithm

Propose a representation (e.g., parent pointers, adjacency list) and an LCA algorithm (binary lifting, Euler tour + segment tree, or Tarjan's offline). Explain time/space trade-offs and how to extend to multiple nodes.

3. Design for Dynamic Updates and Thread Safety

Discuss how to handle insertions/deletions of nodes and groups. Suggest thread-safe structures (e.g., ConcurrentHashMap, ReadWriteLock) and strategies like copy-on-write or versioning to ensure consistency during concurrent reads and writes.

4. Outline Unit Testing Strategy

Describe test cases: single node, two nodes in different subtrees, one node ancestor of another, multiple nodes, dynamic updates, and concurrent access. Mention using mocking for concurrency tests and property-based testing for tree invariants.

5. Summarize and Discuss Trade-offs

Recap the chosen approach, highlighting trade-offs between preprocessing time, query time, memory, and update complexity. Mention potential optimizations and when to use alternative algorithms.

Key Points to Mention

LCA algorithms: binary lifting (O(n log n) preprocessing, O(log n) query), Euler tour + RMQ (O(n) preprocessing, O(1) query), Tarjan's offline (O(n α(n)) for batch queries).
Extending LCA to multiple nodes: iterative pairwise LCA or finding the node with maximum depth that is an ancestor of all (using Euler tour intervals).
Tree representation: parent pointers for simple upward traversal, adjacency list for general trees, or Euler tour for RMQ-based approaches.
Thread safety: use of ConcurrentHashMap for node storage, ReadWriteLock for tree modifications, or immutable snapshots with versioning to allow lock-free reads.
Dynamic updates: handling node/group insertions and deletions by updating parent pointers and rebalancing if necessary; consider using a dynamic tree data structure like link-cut trees for frequent updates.
Unit testing: cover edge cases (empty tree, single node, nodes not in tree), multiple nodes, dynamic updates, and concurrency (using stress tests with multiple threads).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.