Ibm Data Analyst Interview Guide

The interview process at IBM felt pretty dependent on the client, but in my case it was a fairly standard data analyst loop with a mix of screening, technical, and HR conversations. The first round was a pre-screening call where they spent most of the time asking about my current role, my responsibilities, and whether my background fit the position. That part was more conversational than technical, and there was also a small task in the opening round that took about 30 minutes.

The technical round was the real filter. It lasted about an hour and centered on SQL, with two main questions broken into several follow-ups. One of the questions was about calculating the percentage of fraudulent transactions from a table, so they were clearly testing how I handled aggregations and basic business metrics. In another interview, the SQL focus was described as mostly joins and pulling specific data, which matches what I saw. I also got a few Python questions, and those were more mixed: one person may find them generic, but in my case they felt more challenging than the SQL, especially when the questions leaned into harder LeetCode-style patterns like sliding window problems. After that, there was an HR round that was about an hour and mostly covered fit and general background. The interviewers were friendly overall, and the technical difficulty ranged from easy to moderate on SQL, but Python could get unexpectedly hard. I didn’t move forward in the process, so I’d say the main takeaway is to be ready for both practical SQL joins/metrics questions and at least one Python round that may be more algorithmic than you’d expect for a data analyst role.