Databricks SWE Interview: Technical Coding Guide

Estimated read time: 8-10 minutes

Summary: The Databricks SWE technical coding screen is a high-signal engineer-led round, commonly around 60 minutes. The source material is unusually concrete: expect runnable coding, production-quality code, tests, edge cases, Big-O, clean organization, and follow-up constraints. This guide translates the reported questions into candidate-facing tasks.

TL;DR + FAQ (read this first)

At-a-glance takeaways

• The technical coding screen is commonly reported around 60 minutes.
• Expect a Databricks engineer and CoderPad or a similar runnable IDE.
• Official prep emphasizes production-quality code, comprehensive tests, edge cases, Big-O, and language fluency.
• Reported questions include trees, IP/CIDR rule matching, Tic Tac Toe variants, House Robber, graph/BFS, merge sorted lists, and priority queue design.
• A single failing test or vague assumption can hurt because the round is implementation-heavy.

Quick FAQ

Can I run code?
The source points to CoderPad or a similar runnable IDE, but you should still explain and test manually.

Is this only algorithms?
Mostly coding and DSA, but Databricks cares about production quality, tests, and clean organization.

Which language should I use?
The official prep says language agnostic, but expects fluency in your chosen language.

Do senior candidates still get coding?
Yes, coding appears across levels, with deeper tradeoffs for senior roles.

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1) Format and setup

The research describes a 60-minute coding screen with a Databricks engineer. The format is usually CoderPad or a similar online IDE where code can run. Databricks expects fluency in your chosen language, not just pseudocode.

The official prep material raises the bar beyond "find an algorithm." You need readable code, edge-case coverage, tests, and Big-O analysis. Treat the round like a small production implementation under interview time.

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2) Questions you may face in the technical screen

The source includes exact and representative coding questions. The wording below is shaped like a live interview task, including follow-ups where the research supports them.

• You are given a Fibonacci tree. Find the shortest path between two nodes. Now explain how your approach changes if you can enumerate the tree in order.
• Implement access_ok for allow and deny IP or CIDR rules. Return whether an IP is allowed, then print the matching rule index.
• Design Tic Tac Toe for an n by m board where a player wins with k tokens in a row. Then add a random AI player.
• Solve House Robber. State the recurrence, implement it, and test the empty, one-house, and all-equal cases.
• Given a graph, use BFS to find the requested path or reachability result. Then handle cycles and disconnected components.
• Merge sorted lists efficiently. Explain the data structure you use when the number of lists grows.
• Implement a priority queue using a linked list. Then discuss the cost of insert and pop.
• Given a friendship timeline, answer when people become connected or when a condition first becomes true.

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3) What Databricks is looking for

Strong candidates clarify requirements, write clean code, run meaningful tests, cover edge cases, and analyze complexity. Databricks official prep also calls out code organization, separation of concerns, and production-quality implementation.

Senior candidates should not skip fundamentals. The difference is that follow-ups may probe tradeoffs, maintainability, and how the code would behave as requirements grow.

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4) Failure modes

No tests. The official prep explicitly values comprehensive test coverage.

Weak edge cases. Empty input, overlapping CIDR ranges, invalid boards, cycles, and disconnected graphs can break otherwise plausible code.

Vague assumptions. State the input contract before you rely on it.

Messy code organization. Production-quality code matters here.

No Big-O analysis. Complexity is part of the expected signal.

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5) How to prepare

• Practice coding in a runnable editor and write tests as you go.
• Drill trees, graphs, dynamic programming, hash maps, priority queues, and simulation.
• For every solve, add edge-case tests before you call it done.
• Practice explaining Big-O and why the code is organized the way it is.
• For senior candidates, discuss maintainability and follow-up tradeoffs without overengineering the base solution.

The screen rewards the engineer who can build a small, correct, tested piece of software under time pressure.

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