IBM Data Analyst candidates report a mix of assessment, behavioral, HR, and technical conversations, with preparation centered on project explanations, practical data work, SQL metrics, and Python.
$119K
Avg. Base Comp
$143K
Avg. Total Comp
3-4 rounds
Typical Rounds
3-5 weeks
Process Length
IBM Data Analyst interviews in these reports combine conversation about your background with practical technical evaluation. One candidate described an online assessment followed by behavioral, HR, and technical stages, while another described a pre-screening conversation, a technical round, and an HR discussion. Be ready to explain your past projects and one challenging project clearly, including the obstacles you faced and how you addressed them; that theme appeared in both the behavioral and technical portions of the reports.
Technical content varied by candidate. One report included a practical Excel parsing prompt, suggesting that clear reasoning through everyday data tasks can matter alongside theory. Another candidate encountered SQL questions about calculating the percentage of fraudulent transactions and using joins to retrieve data. That candidate also reported Python questions that ranged from general prompts to harder sliding-window-style problems. Practice communicating assumptions and working through the steps of a solution, rather than preparing only polished final answers.
The available accounts are limited and describe different paths, so the exact sequence and technical depth may vary. For the strongest coverage, prepare a concise project narrative, SQL for joins and business metrics, a practical spreadsheet/data exercise, and Python fundamentals that can extend into algorithmic reasoning.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Ibm process.
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.
Prep tip from this candidate
Drill SQL questions that ask you to compute business metrics from a table, especially percentages and join-based pulls. Also be ready for a Python screen that can jump from generic questions into harder sliding-window style problems.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
| Question | |
|---|---|
| 500 Cards | |
| Prime to N | |
| Largest Salary by Department | |
| Find the Missing Number | |
| Raining in Seattle | |
| Impression Reach | |
| Encoding Categorical Features | |
| Lazy Raters | |
| Top 5 Turnover Risk | |
| P-value to a Layman | |
| Fair Coin | |
| Total Transactions | |
| Found Item | |
| Ride Coupon | |
| Estimated Rounds | |
| Expected Tests | |
| Missing Housing Data | |
| Three Zebras | |
| Flatten JSON | |
| Valid Anagram | |
| Binary Tree Conversion | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Secret Wins | |
| Median Probability | |
| Biased five out of six | |
| Swap Variables | |
| Slow SQL Query |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported beginning with an online assessment before later conversations. The report does not describe its content, so candidates may want to confirm the format with their recruiter rather than assume a specific assessment type.
Candidates report conversations about their current role, responsibilities, previous experience, projects, interests, and a challenging project. Prepare a clear introduction and specific examples that explain both your contribution and how you handled obstacles.
Technical content varied across reports. One candidate received an Excel column-parsing question; another reported SQL on fraud-rate calculation, joins, and data pulls, plus Python that could become sliding-window style. Expect the emphasis to vary by interview.
Both reports reference an HR or fit-focused conversation. Candidates report questions about background and fit, so connect your experience and project examples to the analyst role in a concise, consistent story.
