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Databricks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Databricks software engineer round focused on a generalized Tic-Tac-Toe design problem. Fairly implementation-heavy with a follow-up that pushed into async/API territory, which I did not fully anticipate.

Questions Asked (2)

Q1

Implement a generalized Tic-Tac-Toe game where the board dimensions and win condition (k-in-a-row) are configurable. Build a Game class with methods for making a move, checking for a win, and returning the current board state.

Algorithms & Data StructuresSystem Design
Author's notes

Spent way too long bikeshedding the constructor signature before writing any real code.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then design the Game class with a 2D board and configurable dimensions and win condition. Implement move validation, win checking by scanning from the last move in all directions, and a method to return the board state. Discuss time/space complexity and potential optimizations.

Pro tip: Demonstrate awareness of scalability by mentioning that win checking can be optimized to O(k) per move instead of O(n^2) by only checking lines through the last placed piece, and discuss how this design could extend to a distributed setting.

1. Clarify Requirements and Edge Cases

Ask about board size limits, number of players, input validation, and what constitutes a win (exactly k or at least k). Confirm expected behavior for invalid moves and draw conditions.

2. Design the Game Class and Data Structures

Define the class with a 2D array for the board, current player, and configurable parameters (rows, cols, k). Consider using a 1D array for efficiency and discuss trade-offs.

3. Implement Core Methods

Implement makeMove (validate, place piece, switch player), checkWin (scan from last move in all 8 directions), and getBoardState (return a copy or string representation).

4. Analyze Complexity and Optimize

Explain that win checking is O(k) per move by only checking lines through the last move, and overall space is O(n^2). Discuss potential optimizations like bitboards for small boards.

5. Test and Discuss Extensions

Walk through test cases (win, draw, invalid move) and mention how to extend to a distributed system (e.g., using a central server or consensus) if relevant to Databricks' scale.

Key Points to Mention

  • Configurable board dimensions and win condition (k-in-a-row)
  • Efficient win checking by scanning only from the last move in all 8 directions
  • Time complexity: O(k) per move for win check, O(1) for move placement
  • Space complexity: O(n^2) for board storage
  • Input validation and error handling for invalid moves
  • Potential optimizations: bitboards, early termination, and distributed considerations

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

Q2

Follow-up: extend the game to support an AI player that picks its move by calling a provided random API to select a legal (unoccupied) cell.

API & IntegrationsTechnical Trade-offs
Author's notes

This one surprised me a bit.

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AI HintsAI Generated

Suggested Approach

First, clarify the interface of the random API and the game state representation. Then, design a function that collects all unoccupied cells, uses the random API to pick an index, and returns the corresponding move. Finally, discuss edge cases like no legal moves and how to integrate the AI player into the game loop.

Pro tip: Mention that you would inject the random API as a dependency to make the AI player testable and deterministic in tests. Also, consider performance if the board is large, but for typical board sizes, collecting legal moves is fine.

1. Clarify requirements and API

Ask about the random API's signature (e.g., returns a float in [0,1) or an integer in a range) and the game state's representation (e.g., 2D array, list of moves).

2. Design the AI move selection

Outline a function that gathers all unoccupied cells, uses the random API to select one uniformly, and returns the move. If no legal moves, handle gracefully (e.g., return null or throw).

3. Integrate with game loop

Explain how the AI player's turn is triggered, how the move is applied, and how to alternate turns with a human player.

4. Address edge cases and testing

Discuss handling full board, invalid moves, and how to test the AI with a mock random API to ensure deterministic behavior.

Key Points to Mention

  • Uniform random selection among legal moves
  • Dependency injection of random API for testability
  • Handling no legal moves (e.g., game over)
  • Efficiency of collecting legal moves (O(n) for n cells)
  • Separation of concerns: AI logic vs game state management
  • Potential for extending to different AI strategies (e.g., minimax)

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