IBM Data Analyst candidates report a structured 3-4 round process covering background, behavioral fit, SQL business metrics, practical data work, and sometimes Python.
$80K
Avg. Base Comp
$120K
Avg. Total Comp
3-4 rounds
Typical Rounds
3-5 weeks
Process Length
IBM Data Analyst candidates report interview loops that combine conversational screening with practical technical assessment. One reported path moved through an online assessment, behavioral conversation, HR discussion, and technical interview; another began with a pre-screen and short task before technical and HR conversations. Project storytelling is a recurring theme: candidates were asked to introduce themselves, explain prior projects and interests, and describe a difficult project and how they handled obstacles.
The technical content varies by interview. Reported SQL questions included calculating the percentage of fraudulent transactions from a table, using joins, and retrieving specific data. One candidate also received a practical Excel column-parsing question. Python may appear alongside SQL, and one report described questions escalating toward sliding-window-style problems even though SQL felt easy to moderate. Prepare clear explanations of your work, then practice translating a business metric into SQL and reasoning aloud through a small data task.
The available reports are limited, so exact sequencing and technical depth may differ by team or client. HR and behavioral conversations focused on background, responsibilities, fit, and prior experience; candidates described interviewers as friendly and the overall flow as structured.
Synthesized from 2 candidate reports by our editorial team.
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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 | |
| Total Transactions | |
| Fair Coin | |
| Found Item | |
| Ride Coupon | |
| Hurdles In Data Projects | |
| Flatten JSON | |
| Valid Anagram | |
| Estimated Rounds | |
| Find Duplicate Numbers in a List | |
| Binary Tree Conversion | |
| Missing Housing Data | |
| Target Indices | |
| Expected Tests | |
| Three Zebras | |
| Median Probability | |
| Biased five out of six | |
| Secret Wins | |
| Swap Variables | |
| Slow SQL Query |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report either an online assessment or a pre-screening call at the start. The call focused on current responsibilities and fit; one candidate also completed a small task that took about 30 minutes.
Candidates report questions about introductions, past projects, area of interest, prior experience, and a challenging project. Expect to explain your role, the obstacles you faced, and how you addressed them.
Reported technical questions included SQL joins, targeted data pulls, and calculating a fraudulent-transaction percentage. One candidate encountered Excel column parsing, while another reported Python questions that could include harder sliding-window-style patterns.
Candidates report an HR round focused on fit and general background. In one account it lasted about an hour; prepare to connect your responsibilities and project experience to the analyst role.