IBM Data Analyst interviews, as three candidates describe them, combine an online or coding assessment, SQL and Python questions, behavioral conversations, and an HR round. Prepare project stories and timed practice.
$96K
Avg. Base Comp
$110K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Three candidates describe IBM Data Analyst interviews that mix a screening or assessment step with behavioral and HR conversations. The technical depth varied. One candidate who received an offer described an online assessment, a behavioral round, an HR round and a technical interview. That technical round focused on past projects, a challenging project, and a practical question about parsing an Excel column-wise.
Another candidate, who did not receive an offer, reported a pre-screening call with a roughly 30-minute task. A one-hour technical round followed, centered on SQL, including a question on the percentage of fraudulent transactions. The same candidate found the Python questions harder than the SQL, with sliding-window style problems, and then had an HR round. That candidate also said the process seemed to depend on the client.
A third candidate, who also did not receive an offer, described a 30-minute coding assessment covering entry-level Python and SQL. The Python portion had 15 test cases, two with runtime requirements, so a correct answer that ran too slowly was not enough. Behavioral questions covered why they wanted to build a career at IBM, a decision made with incomplete information, and a time collaborating with others.
In practice, prepare for three things: SQL aggregation and business-metric questions, timed Python practice, and specific project and STAR-style stories. The sample is small, so expect the exact format to vary by team.
Synthesized from 3 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 | |
| Missing Housing Data | |
| Expected Tests | |
| Three Zebras | |
| Valid Anagram | |
| Estimated Rounds | |
| Flatten JSON | |
| Hurdles In Data Projects | |
| Binary Tree Conversion | |
| Find Duplicate Numbers in a List | |
| Target Indices | |
| Secret Wins | |
| Median Probability | |
| Biased five out of six | |
| Swap Variables | |
| Slow SQL Query |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report different openers. One described an online assessment, another a pre-screening call about their current role with a task of about 30 minutes, and a third a 30-minute Python and SQL coding assessment. Practice timed entry-level Python and SQL; the format may vary by team.
Candidates report questions about past projects, a challenging project and how they overcame it, and why they wanted to build a career at IBM. Another candidate was asked about deciding with incomplete information and collaborating with others. Prepare specific stories with your reasoning and the outcome.
One candidate reported a one-hour technical round centered on SQL, with a fraudulent-transaction percentage question and follow-ups. Python was reported as harder, including sliding-window style problems. Another candidate with an offer described a practical Excel column-wise parsing question alongside project discussion. Depth appears to vary.
Two candidates mention an HR round, one of about an hour covering fit and general background. One candidate who received an offer said it came about a month later. The other two candidates reported no offer. Experiences may differ by team or client.
