Start by clarifying the requirements: what operations are needed (e.g., find manager, list direct reports, find all reports, find common manager), and whether the hierarchy is static or dynamic. Then propose a data structure, such as a tree with parent pointers or adjacency lists, and discuss trade-offs for each operation. Finally, outline how to implement key queries efficiently, possibly using additional structures like a hash map for quick lookups.
Pro tip: Demonstrate awareness of real-world constraints: hierarchies can be deep, so recursion may cause stack overflow; mention iterative approaches or tail recursion. Also, consider that employees may have multiple managers in matrix organizations, so clarify if it's strictly a tree.
Ask about the operations needed (e.g., find manager, direct reports, all reports, common manager), whether the hierarchy is a tree or DAG, and if updates are frequent. This shows you don't jump to solutions without understanding the problem.
Suggest a node-based tree where each employee has a pointer to their manager and a list of direct reports. Alternatively, use an adjacency list (hash map from employee ID to list of report IDs) for flexibility. Discuss trade-offs.
For each required operation, explain how to implement it and its time/space complexity. For example, finding all reports can be done via DFS/BFS; finding common manager can use parent pointers and a set.
If certain queries are frequent, propose augmentations like caching or additional pointers (e.g., skip pointers) to speed them up. Mention that you'd profile to decide.
Discuss handling cycles (if not a tree), deep hierarchies (iterative traversal), and large datasets (distributed storage or database modeling).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.