IBM Data Engineer candidates describe project-centered technical discussions, data-platform and SQL scenarios, and sometimes a manager or HR conversation that includes compensation.
$129K
Avg. Base Comp
$173K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
IBM Data Engineer interviews in these accounts center on whether you can explain real data work with enough technical and practical detail. Prepare your project narrative at the level of your own decisions and contribution. Candidates describe being asked about previous projects, the data platforms and services they used, a difficult project, and how their background fits the role. A clear explanation of governance choices, troubleshooting, and responsibilities can therefore be as important as a broad project overview.
Technical coverage varies. One candidate reported scenario-based SQL and data-modeling discussion alongside data warehousing, ETL, reporting, Python, PySpark fundamentals, and debugging. Another encountered a warehouse-versus-data-lake comparison, Spark architecture and lazy evaluation, SQL, a basic Python string task, generative-AI questions, and a maximum-length bitonic-subarray problem. This means preparation should combine practical platform reasoning with baseline coding readiness rather than assume a purely algorithmic interview.
Candidates also report a manager-facing discussion of role responsibilities, experience, and sometimes compensation; one process had scheduling problems and a planned final conversation was cancelled before an offer. Evidence is limited to a small set of accounts, so the sequence may differ by team. Be ready to describe concrete trade-offs in data systems, reason through SQL and modeling scenarios aloud, and state compensation expectations clearly if the conversation reaches that point.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Ibm process.
Honestly, the technical round was pretty basic and much more focused on my own projects than on difficult DSA. I had one technical interview where the questions were tied to work I had already done and my previous experience. The conversation was fast-paced and direct, so I needed to explain what I had worked on, what my responsibilities were, and how my background matched the role. Nothing in my process felt like a heavy algorithm round, although the exact technical format at IBM can depend a lot on the interviewer.
After that, I had a manager round. This was less technical and included HR-style discussion about the role, responsibilities, and my experience. We also talked about salary. They did not finalize the compensation immediately during the interview; the team contacted me later with the amount they could provide. The overall process was straightforward, and the expectations for the position were explained clearly, including the emphasis on continuing to learn after joining.
I received and accepted the offer. My main takeaway is to know your projects properly because the technical discussion can stay very close to your resume. Be ready to explain your exact contribution instead of only giving a high-level project summary, and keep your compensation expectations ready for the manager discussion.
Prep tip from this candidate
Review every project on your resume and practice explaining your responsibilities, previous experience, and specific contribution, since the technical questions were based mainly on project work. Also prepare a clear salary expectation because compensation came up in the manager round and was finalized later.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Ibm
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Largest Salary by Department | |
| Prime to N | |
| Find the Missing Number | |
| Top 5 Turnover Risk | |
| The Brackets Problem | |
| String Mapping | |
| Cyclic Detection | |
| Valid Anagram | |
| Missing Housing Data | |
| Flatten JSON | |
| Find Duplicate Numbers in a List | |
| Target Indices | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| New Resumes | |
| Total Transactions | |
| Move Zeros Back | |
| Transformer Encoder Layer | |
| Swap Variables | |
| Slow SQL Query | |
| Binary Tree Conversion | |
| String Palindromes | |
| Targeted sum | |
| Equal Binary Subarrays | |
| Double Card Value | |
| Find Square Root | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Justify a Neural Network |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening conversation about their background and previous projects. Expect to explain responsibilities, tools used, a challenging project, and your specific contribution; the discussion may stay close to the resume rather than follow a fixed coding format.
Reported technical content includes data warehouses versus data lakes, ETL and reporting, governance, data modeling, SQL scenarios, Spark architecture and lazy evaluation, PySpark debugging, and basic Python. One candidate also reported a maximum-length bitonic-subarray problem and generative-AI questions, so coding difficulty may vary.
Candidates describe a manager-facing discussion of role responsibilities, experience, and compensation, with an HR stage mentioned in one account. Scheduling and the exact closing sequence may differ: one candidate's planned final round was cancelled before an offer was made.
