The Databricks behavioral round is usually a hiring manager interview on your past projects, scored against the company’s six published culture principles: customer obsessed, raise the bar, truth seeking, first principles, bias for action and company first. The questions asked most are why Databricks, a walkthrough of your internship or most complex project, a time you made a decision from data, a time you moved fast with incomplete information, and a conflict with a teammate.
In the Databricks interview process this is usually the last onsite round. A US engineer who accepted an offer in November 2025 had it after DSA, systems and coding rounds, and a Mountain View intern in October 2025 had a purely behavioral hiring manager round after two coding interviews. Full loops are on the Databricks interview experience page, and the technical rounds are on the Databricks interview questions hub.
Databricks’ six culture principles
Databricks publishes these on its careers site:
| Principle | Databricks’ wording, paraphrased | Story to prepare |
|---|---|---|
| We are customer obsessed | What is best for the customer is best for Databricks | You changed a plan because of a user’s real problem |
| We raise the bar | Every day is a chance to do better | You set a higher standard and kept it |
| We are truth seeking | Decisions are based on data and change as data changes | Data overturned your first idea |
| We operate from first principles | Start with why | You rebuilt an approach from the actual requirement |
| We bias for action | Debate, plan, execute and iterate with urgency | You shipped under time pressure and deferred the rest on purpose |
| We put the company first | Do what is best for the company | You gave something up for a bigger goal |
Common Databricks behavioral questions
1. Tell me about yourself
Keep it to 60-90 seconds: current role or degree, one project with a number attached, and why Databricks now. Example: “I’m finishing a CS degree and interned on a data pipeline team, where I cut a nightly Spark job from 3 hours to 50 minutes by fixing a skewed join. I want to build the systems underneath tools like that, which is why I applied to Databricks’ infrastructure teams.”
2. Why Databricks?
Name something specific you have used or read: Delta Lake’s transaction log, Unity Catalog, Photon, or an open-source project Databricks started such as Apache Spark or MLflow. Then connect it to the team you applied to. Avoid saying it is a fast-growing company; every candidate says that.
3. Walk me through your previous internship and how it ended
Recruiters ask this directly; a September 2025 Bellevue new grad was asked what they built and how the internship ended. Give the project, your part of it, the measurable result, and the outcome (return offer, project shipped, or what was left unfinished and why).
4. Tell me about a time you put the customer first even when it cost your team time
This maps to “we are customer obsessed.” Example: “Our internal analytics users kept rerunning a slow report. I spent two sprint days adding incremental refresh instead of the feature on our roadmap. Report time fell from 20 minutes to under 2, and the request queue for that team dropped by half.”
5. Tell me about a time you raised the bar on code or on hiring
Maps to “we raise the bar.” Pick a concrete standard you introduced: required tests for a flaky module, a design review template, or interview feedback you pushed to make more specific. State the before and after, such as on-call pages per week or review turnaround.
6. Tell me about a decision you made based on data that went against your intuition
Maps to “we are truth seeking.” Example: “I was sure caching would fix our API latency. Profiling showed 70% of time was in serialization, so I switched the serializer instead. p95 dropped from 900 ms to 300 ms. I would have wasted a week on a cache.”
7. Describe a problem you solved by going back to first principles
Maps to “we operate from first principles.” Show that you questioned an assumption, for example why a batch job needed to run hourly at all, and rebuilt the approach from the actual requirement. Say what you measured to prove the new design worked.
8. Tell me about a time you moved fast with incomplete information
Maps to “we bias for action.” Use Situation, what you did not know, what you shipped, and what you deliberately deferred. Example: shipping a feature flag and a rollback plan the same day an incident hit, then writing the full fix the next week.
9. Tell me about a time you put the company’s goal ahead of your team’s
Maps to “we put the company first.” Good stories involve giving up headcount, a project, or credit so another team could ship something more important. Explain how you made the trade-off and what happened to your own work.
10. Tell me about the most technically complex project on your resume
Hiring managers go deep here, so pick a project where you can explain the architecture, the hardest bug, and the trade-off you would change. Have numbers ready: data size, latency, users, or cost.
11. Tell me about a time you disagreed with a teammate or manager
Show that you used evidence rather than seniority to settle it, which fits truth seeking. Describe the disagreement, the data you gathered, the decision, and whether you were right. Admitting you were wrong is fine if you show what changed afterward.
12. Describe a failure and what you changed afterward
Pick a real miss with a cost, such as a bad deploy, a missed deadline or a wrong estimate. Spend most of the answer on the fix and the habit you changed, for example adding a canary stage or splitting estimates into smaller tasks.
13. What questions do you have for us?
Ask about the team’s on-call load, how projects get chosen, how the team measures customer impact, or how new grads get their first project. Asking how the culture principles show up in performance reviews is also reasonable.
Answers that hurt in the Databricks behavioral round
- Claiming an outcome with no number behind it, which cuts against truth seeking
- Moving fast with no rollback or follow-up plan; bias for action still expects judgment
- Not being able to explain the technical detail of your own project when the hiring manager drills in
- Describing teamwork in general terms without a decision you personally made
- Confusing Databricks with a reseller of Spark; know what the platform does now (lakehouse storage, governance, AI and SQL warehousing)
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