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Databricks interview notes, page 4 of 22

Large Enterprises

Most of these Databricks write-ups come from Software Engineers (192), then Machine Learning Engineers (9) and Data Scientists (8). The most reported rounds are the technical phone screen (136), the system design round (57), the behavioral / leadership round (9) and the onsite coding round (6).

216 interview notes · updated Jul 2026

Databricks·Product Manager·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Databricks for a PM role, just the one question about your proudest accomplishment. Pretty standard stuff.
  • What accomplishment are you most proud of?

“I went with a product launch story and structured it as situation, action, result.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks system design round, and they went straight for a distributed file system from scratch. Pretty deep dive covering everything from metadata services to garbage collection. Felt like a lot of ground to cover in one session.
  • Design a distributed file system that exposes CRUD APIs (including delete) and scales horizontally.

“I started with functional requirements which felt right but I spent too long on the basics like open/read/write and didn't get to the interesting stuff fast enough.”

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Databricks·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Databricks ML engineer interview with a from-scratch coding problem on linear regression. Pretty technical, they wanted the full implementation with no shortcuts, and the follow-ups were designed to catch gaps in your actual understanding rather than just whether you got the code running.
  • Implement linear regression with gradient descent from scratch, no ML libraries. Should predict outputs from a feature matrix, compute MSE loss with proper normalization, update weights with a configurable learning rate, and support early stopping.
  • What happens to gradient descent when the learning rate is too large or too small?
  • How would you normalize features before training, and why does it matter for gradient descent?
  • Here is a buggy implementation of gradient descent. Find and fix the bugs.

“This took longer than I expected.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a data engineering role at Databricks and got hit with some architecture fundamentals pretty early on. Nothing too wild but it made me realize I'd been fuzzy on a few distinctions I thought I had locked down.
  • What is the difference between a data lakehouse and a data warehouse, and when would you choose one over the other?

“I had a decent answer for the first part but stumbled a bit on the tradeoffs portion.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks SWE interview that looked like a straightforward LRU Cache problem until the QPS monitoring requirement showed up and made the whole thing a lot more interesting. The real test was whether you'd reach for the obvious O(n) approach or think through the bucketing angle.
  • Build a key-value store with get and put operations, and add a sliding-window QPS metric that returns queries per second for each operation type over the last 5 minutes.

“Started with the LRU cache part fine, that's just a hashmap plus doubly linked list.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks coding screen, one algorithmic problem on grid traversal. Pretty standard stuff but the edge cases will get you if you're not careful.
  • Given a 2D grid with a start cell, a destination cell, open roads, and blocked cells, find the shortest path from start to destination moving only in four directions. Return -1 if no path exists.

“Classic BFS setup.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks coding round focused on run-length encoding, which sounds like a warmup problem until you actually get into the edge cases and realize there's a lot more to discuss than the basic compress/decompress logic.
  • Implement run-length encoding: write an encode function that compresses a string by replacing consecutive identical characters with the character and its count, then write a decode function that reverses it. Be prepared to discuss edge cases like single characters, multi-digit counts, runs exceeding some cap, and input strings that contain digit characters.

“I got the basic encode working pretty fast but decode is where things got messy.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks coding round focused on a classic game logic problem, but the real test was whether you'd spot the optimization angle. Pretty standard for a mid-to-senior engineering screen.
  • Design and implement a Tic-Tac-Toe class with a move(row, col, player) method that returns the current game state after each move. How do you make each move check run in O(1) time?

“I got the basic implementation working fine, class with a 2D board, checking rows and columns and diagonals after each move.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks software engineer interview with a grid BFS problem. Pretty clean problem once you see the multi-source angle, but I definitely overthought the initialization phase for a bit.
  • Given a city grid where cells are either open streets, buildings, or transit stations, find the minimum number of moves from each open street cell to the nearest transit station. Buildings and unreachable cells return -1.

“My first instinct was to BFS from every open cell individually and that would've been a disaster at 300x300.”

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Databricks·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Databricks for a software engineering role. Pretty standard culture fit stuff, nothing that'll trip you up if you've done a few of these before.
  • Why do you want to work at Databricks specifically?
  • Tell me about a time you received critical feedback. How did you respond to it?
  • What's the hardest technical or work problem you've faced, and how did you solve it?
  • How do you approach breaking down a large, complex task or project into manageable pieces?

“Prepped this one pretty well so it felt fine.”

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