Databricks Solutions Engineer Interview Questions
& Process

For me it was just three rounds of interview which were back to back. First, the HR/Manager called and did a phone interview with me. After that, there were some technical questions on networking, operating system, and cloud. They also asked me questions on my experience.

I interviewed for the Amsterdam role. The interview had 1 hiring manager interview, 3 technical discussions (30 mins each), 2 behavioural (30 mins, culture fit) and 1 very long assignment which takes days to do properly. I was exhausted by the process and they refused to tell me the salary. I think that was the first red flag that I ignored back then. In the end they sent a very generic rejection that referred back to the first tech discussion I had. So they put me through all the stages with 0 intention of ever hiring me. The worst thing was that as part of the assignment I had to log in to Databricks to finish it and I gave my information (email, phone..). Within 1 week a sales person called me and asked to speak to my manager to sell Databricks products to my company. I told him that I was using Databricks only for the interview, he didn’t care. I have blacklisted this company. I am just sharing this experience to warn other people what to expect.

The interview process had five structured rounds, each aimed at testing a different side of my skills and overall fit: 1. Hiring Manager Round: This was a conversational chat about my current role, project scope, and my day-to-day work. The interviewer dug into my experience with the data stack and how I add value in the broader business context. 2. Technical Assignment (SQL + PySpark): I got a take-home assignment with real-world SQL and PySpark tasks. The emphasis was on writing clean, efficient code for data transformation and aggregation, while also dealing properly with edge cases. 3. Technical Deep-Dive (Live Interview): This was an in-depth technical round going through my resume projects and topics like Virtual Networks (VNet), CI/CD practices, Git workflows, basics of ML model deployment, and other cloud engineering areas. The interviewer evaluated both my depth and breadth. 4. Project Presentation Round: I had to present one of my past projects end-to-end. I covered the architecture, tools used (ADF, ADLS, Databricks, Snowflake), issues I ran into, how I solved them, key learnings, and my own technology preferences. The panel looked at how clearly I can communicate technical choices. 5. Executive Fit/Client Communication Round: The last round was about stakeholder management and soft skills. I got behavioral questions on handling difficult client asks, ambiguity, ownership, and real client conversations. The focus was on how I explain complex data topics to non-technical stakeholders and how I work with others under pressure.