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Bloomberg·Software Engineer·Technical Phone Screen·Senior

SeniorPending
Aug 2026London

Summary

Interviewing for a senior software engineering role at Bloomberg London. Cleared the recruiter screen and a technical phone screen with two coding questions no problem, now prepping for a coding round and a 90-minute system design round and not totally sure what to expect from either.

Questions Asked (2)

Q1

Given an array, solve a problem involving array manipulation or traversal.

Algorithms & Data Structures
Author's notes

Went through this one pretty smoothly.

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AI HintsAI Generated

Suggested Approach

Clarify the problem requirements and constraints first, then discuss multiple approaches with trade-offs, and finally implement the optimal solution with clean code and test it with edge cases.

Pro tip: At Bloomberg, interviewers value not just correctness but also efficiency and scalability; always analyze time and space complexity and mention how your solution would handle large datasets.

1. Understand the problem

Ask clarifying questions to confirm input format, output expectations, constraints, and edge cases. Restate the problem in your own words to ensure alignment.

2. Brainstorm approaches

Think aloud about possible solutions, from brute force to optimized, and discuss their time and space complexities. Consider data structures that could simplify the problem.

3. Choose and explain the best approach

Select the most efficient approach that balances time and space, and explain why it's optimal. Mention any trade-offs.

4. Implement the solution

Write clean, modular code with meaningful variable names. Handle edge cases and avoid off-by-one errors.

5. Test and verify

Walk through the code with sample inputs, including edge cases like empty arrays, single elements, duplicates, and large inputs. Discuss potential optimizations if needed.

Key Points to Mention

  • Time and space complexity analysis for each approach
  • Edge cases such as empty array, single element, duplicates, and negative numbers
  • Choice of data structures (e.g., hash maps, two pointers, sorting) and their impact
  • Trade-offs between different solutions (e.g., time vs. space)
  • Scalability and performance considerations for large datasets
  • Code readability and maintainability

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

Q2

Given a tree structure, solve a problem involving tree traversal or operations.

Algorithms & Data Structures
Author's notes

Also got through this without much trouble.

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AI HintsAI Generated

Suggested Approach

First, clarify the exact tree problem and constraints (e.g., binary tree vs. n-ary, node values, expected time/space complexity). Then, choose the appropriate traversal (DFS or BFS) and outline a recursive or iterative solution, discussing trade-offs. Finally, walk through a small example to verify correctness and analyze complexity.

Pro tip: At Bloomberg, interviewers value clean, efficient code and clear communication. Before coding, explicitly state your assumptions and ask clarifying questions; this demonstrates thoroughness and reduces the risk of solving the wrong problem.

1. Clarify the problem

Ask questions to understand the tree structure, input/output format, constraints, and edge cases (e.g., empty tree, single node, skewed tree).

2. Choose traversal strategy

Decide between DFS (pre-order, in-order, post-order) and BFS based on the problem requirements, and justify your choice.

3. Design the algorithm

Outline the steps of your solution, including base cases and recursive/iterative logic, and discuss time and space complexity.

4. Implement and test

Write clean code, then walk through a small example to verify correctness and handle edge cases.

5. Optimize if needed

Consider potential optimizations (e.g., iterative vs. recursive, early termination) and discuss trade-offs.

Key Points to Mention

  • Tree traversal types: pre-order, in-order, post-order, level-order
  • Recursive vs. iterative implementation and stack/queue usage
  • Time and space complexity analysis (e.g., O(n) time, O(h) space for DFS)
  • Handling edge cases: empty tree, single node, skewed tree
  • Use of auxiliary data structures (e.g., hash map for caching, queue for BFS)
  • Trade-offs between different approaches (e.g., DFS vs. BFS memory usage)

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