Ing-Diba Data Analyst interview typically runs 3 rounds: HR screening, first interview, final behavioral interview. It can move quickly, with live SQL appearing early and the process feeling more intense than a standard screening.
$92K
Avg. Base Comp
$98K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
We’ve seen ING-DiBa lean hard on careful SQL reasoning rather than broad analytics theory. In the candidate experience we reviewed, the interviewer didn’t just ask for a query — they probed whether two nearly identical statements would return the same result, which is a strong signal that edge-case thinking matters here. That kind of question rewards candidates who can explain joins, filters, and null behavior clearly, not just type quickly under pressure.
A recurring theme is that the technical bar is paired with a real interest in judgment and fit. The same interview that included live SQL also included a character assessment, and the final conversation stayed focused on behavioral alignment. That tells us ING-DiBa is looking for analysts who can be trusted with clean reasoning and steady communication, especially in a finance setting where small mistakes can have outsized consequences.
We’d also pay attention to the fundamentals they chose to surface: data normalization came up alongside SQL, which suggests they care about whether candidates understand how data should be structured, not only how to query it. Our candidates report that the process can feel more intense than expected early on, so the best preparation is being ready to defend your logic in real time when the interviewer pushes beyond syntax into why the query behaves that way.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Ing-Diba process.
The part that stood out to me most was how business-focused the process was from the very beginning. I went through a four-stage interview process that started with an online test focused on analytical skills and business sense, followed by two case-based interviews and a final conversation that felt more like a fit and motivation check than a purely technical screen. The cases were less about trick questions and more about how I structured my thinking, communicated clearly, and connected my analysis to client value.
One of the questions I remember most was how I would create value for clients from day one, given the company’s positioning and the sector-specific challenges its clients face. That question set the tone for the process: the interviewers wanted to see whether I could think commercially rather than simply run numbers. Overall, the interviews felt smooth and natural. I did not feel that the interviewers were trying to trip me up; they were more interested in whether I could reason through practical situations calmly, explain my approach clearly, and connect my recommendations to business impact. My main takeaway is to prepare for case-style questions tied to client value, have a concise explanation of why I want the role, and be ready to describe how I could add value quickly.
Prep tip from this candidate
Practice answering client-value case questions out loud, especially prompts like how you would create value from day one. Also be ready to discuss your SQL level, Excel, and, if the role is fraud-related, AML/KYC and practical fraud scenarios clearly and concisely.
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Topics based on recent interview experiences.
Featured question at Ing-Diba
How do we deal with the missing square footage data to construct our model
| Question | |
|---|---|
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Slacking Employees Salaries | |
| Experiment Validity | |
| Compute Deviation | |
| Find the Missing Number | |
| Subscription Overlap | |
| Bagging vs Boosting | |
| Prime to N | |
| 500 Cards | |
| Session Difference | |
| Last Transaction | |
| Department Expenses | |
| Rain in N Days | |
| Maximum Profit | |
| Like Tracker | |
| Button AB Test | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Paired Products | |
| Google Maps Improvement |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with an HR screening to cover basic background, motivation, and fit for the Data Analyst role. This stage appears to be a standard recruiter conversation before moving into more intensive interviews.
The first substantive interview combines a character or behavioral assessment with live SQL questions. Candidates should expect technical depth early, including SQL reasoning and data fundamentals such as normalization and comparing query outputs.
The last round focuses only on character and behavioral fit. This stage is centered on assessing how the candidate communicates, works with others, and aligns with the team rather than on technical problem-solving.
