Databricks Software Engineer interview typically runs 3-5 rounds: recruiter screen, hiring manager screen, technical coding, system design, behavioral. It usually takes 2-6 weeks and is fast-moving, technical, and communication-heavy.
$168K
Avg. Base Comp
$504K
Avg. Total Comp
4-6
Typical Rounds
3-8 weeks
Process Length
Our candidates consistently report that Databricks is looking for engineers who can move comfortably between code and platform thinking. Even when the round is framed as a coding interview, the follow-ups often push into tradeoffs, complexity, and implementation details rather than just landing the final answer. We’ve seen House Robber, graph traversal, and merge-style problems, but the real separator is whether candidates can explain why their approach holds up under pressure and edge cases. One recurring theme is that a single failing test or a vague assumption can matter a lot, so the bar is not just correctness — it’s precision.
A second pattern is how often the process pulls candidates toward Databricks’ broader data and cloud world. Multiple candidates were asked about OLAP vs. OLTP, data lakes, PII deletion, MLOps, and data pipeline design, which tells us they care about practical platform judgment as much as raw coding ability. We’ve also seen a few experiences skew heavily customer-facing, especially for adjacent roles, where the interviewers cared about explaining technical ideas to non-engineers and handling pre-sales-style scenarios. That means the strongest candidates are usually the ones who can connect their engineering choices to how real data teams operate, not just describe an algorithm in isolation.
Synthetized from 8 candidates reports by our editorial team.
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Real interview reports from people who went through the Databricks process.
I went through Databricks for a Software Engineer role and the process moved pretty quickly, starting with a short recruiter screen that was about 15 minutes. That first call was pretty standard: my background, why I was interested in Databricks, what I was doing in my current role, and salary expectations. The recruiter also gave a clear overview of the teams, the product, and what the rest of the process would look like, which was helpful because the technical bar is clearly high here.
After that, I had technical rounds without any OA. One round was a coding interview on CoderPad where I had to implement from scratch. The question felt a little vague at first and seemed like the kind of thing that gets discussed online, so there wasn’t much hand-holding. Another interview focused on more standard LeetCode-style problems with a current engineer, and I had to explain my thinking as I went. That part was stressful because even one failing test case mattered a lot. The problems I saw were along the lines of Tic Tac Toe and House Robber; the Tic Tac Toe one was mostly implementation-heavy, while House Robber was more straightforward. The last round was a behavioral and resume grill with a senior person or manager, where they dug into past experience and projects and asked why Databricks. Overall it felt less about grinding generic puzzles and more about writing clean code under pressure and being able to talk through tradeoffs. I ended up not getting an offer, and my main takeaway was to be ready for fast-moving interviews where you may need to code, debug, and defend your approach all in the same round.
Prep tip from this candidate
Practice implementing from scratch in CoderPad and be ready to talk through your code line by line, since passing test cases mattered a lot. Also prepare a crisp explanation for why Databricks and for deep questions about your past projects, since the final round leaned heavily on resume and motivation.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Databricks
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Monthly Customer Report | |
| String Mapping | |
| Total Spent on Products | |
| Target Indices | |
| Centralized Event Ingestion | |
| Cumulative Sales By Product | |
| Priority Queue Using Linked List | |
| Text Editor With OOP | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Weighted Average With Missing Dates | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| String Shift | |
| First Touch Attribution | |
| Raining in Seattle | |
| Top 3 Users | |
| Job Recommendation | |
| Minimum Change | |
| Delivery Estimate Model | |
| Find the First Non-Repeating Character in a String | |
| Find Bigrams | |
| Sort Strings | |
| Last Transaction | |
| Size of Joins | |
| The Brackets Problem | |
| Friendship Timeline | |
| P-value to a Layman |
Synthesized from candidate reports. Individual experiences may vary.
A short introductory call with recruiting to cover your background, motivation for Databricks, current role, and salary expectations. The recruiter typically also gives an overview of the team, product, and the rest of the interview loop.
A conversation with the hiring manager focused on your experience, why you want to join Databricks, and whether your background matches the team’s needs. In some loops this stage is mostly behavioral, while in others it also sets up the technical direction of the process.
A live coding or technical problem-solving round, often on CoderPad, with little hand-holding. Candidates reported LeetCode-style problems such as House Robber, graph/BFS questions, and implementation-heavy tasks like Tic Tac Toe, with emphasis on clean code, testing, and explaining tradeoffs out loud.
A deeper technical discussion that can focus on system design, cloud/data architecture, or Databricks-adjacent platform concepts. Reported topics included designing an MLOps system, OLAP vs OLTP, data lake PII deletion, data pipelines, and open table formats.
A round with a senior engineer, manager, or peer where interviewers dig into past projects, career decisions, and how you handle disagreements or ambiguity. Candidates were often asked why Databricks, what they built, and how they communicate technical ideas.
A final loop with multiple interviewers, sometimes including peers, a sales rep, or other cross-functional stakeholders. This stage can mix technical and non-technical evaluation, including situational questions and, in some cases, customer-facing or pre-sales style discussion.