Org-Chart Salary Aggregation

Final-round coding (MLE/AS loop). Part 1: from employee–manager–salary records, return the manager whose direct reports have the highest average salary; a hashmap grouping by manager suffices. Part 2: for a given manager, compute the average salary across the reporting hierarchy — the interviewer hints at a tree structure (recurse over the org chart) rather than a flat group-by. Exact aggregation semantics (direct reports vs full subtree) were left thin and worth clarifying. Requirements Given employee records with salary and manager relationships: Part 1: return the manager whose employees have the highest average salary. Part 2: for a given manager, compute the average salary of their reports — the interviewer steered toward a tree structure over the reporting hierarchy rather than a single flat group-by. Notes Part 1 is a straightforward hashmap group-by: bucket employees under their manager, average each bucket, take the max. A dictionary solution is accepted here. Part 2 is where the tree framing matters: model the org chart as a tree (each manager points to their reports) and recurse. Whether the average is over direct reports only or the entire subtree was left ambiguous — clarify this before coding, since it changes the recursion (aggregate-and-return subtree sum + count vs. one level). Returning (sum, count) up the recursion lets you compute any subtree's average in one post-order pass without recomputation. Watch for cycles / malformed input (an employee listed as their own manager) and employees with no manager (org roots). Preparation Implement Part 1 as a manager→[salaries] map, then Part 2 as a post-order recursion returning (subtree_sum, subtree_count). Prepare a one-line clarifying question on direct-reports-vs-full-subtree averaging; the prompt is deliberately thin on this and the interviewer rewards catching it.