A while back, a recruiter from Databricks reached out about a Solutions Architect position. Since it had some relevance to my background, I figured I'd interview for it and give it a shot, so I wanted to share my fresh-off-the-press interview notes with everyone — hope this helps.
The first round was a behavioral interview with the hiring manager, mainly talking about my past experience along with an introduction to the role and the team. Databricks' culture seems really good — they give new hires 3-6 months, or even longer, of onboarding time, during which a mentor helps you get up to speed on the business and provides training. Compared to other companies where you have to hit the ground running immediately, this is genuinely pretty friendly 🥺. During the conversation the hiring manager asked whether I had used Databricks' products — since they're a cloud-based data intelligence platform, I'd suggest anyone with time should get familiar with the product.
The second round was a technical interview, focused more on the Solutions Architect role itself along with some technical concepts (honestly not too hard). Solutions Architect is the role that helps provide technical support to customers, helping them solve problems related to data quality and integration/system issues. Databricks has a few tracks for this role, and one of them is the data track. Specifically, the interviewer asked a hypothetical question: if a customer meets a challenge about data quality performance, how would you approach it? They also asked about the data storage layer (the difference between a data warehouse and a data lake), data pipeline/solutions, and cloud-related concepts.
The third round was a technical assessment — writing code on CodeSignal (three questions total, with about a week to complete them). It required Python and PySpark, and honestly I found it somewhat difficult.
Hope this helps everyone!
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