Intercontinental Exchange Data Analyst interview typically runs 3 rounds: recruiter screen, technical interview, and final panel. It usually takes a few weeks and is notably SQL-heavy and case-style.
$59K
Avg. Base Comp
$103K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Intercontinental Exchange is looking for analysts who can move comfortably between clean SQL and messy business reality. The technical conversations lean heavily on joins, aggregations, and window functions, but what stands out is that the interviewer wanted the reasoning behind the query, not just the final answer. That tells us ICE is screening for people who can explain tradeoffs clearly and defend an approach when the data gets imperfect.
A recurring theme is the company’s preference for scenario-based thinking. One candidate described a dashboard metric drop investigation that kept evolving as new information was introduced, which is a strong signal that ICE cares about structured troubleshooting under ambiguity. We’ve also seen them probe past analytics work in detail — especially data cleaning choices, assumptions, and how results were communicated. In practice, that means the strongest candidates are the ones who can show they understand both the mechanics of analysis and the operational consequences of getting it wrong, especially in a finance setting where outliers and transaction trends can’t be treated casually.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Intercontinental Exchange process.
I interviewed for a Data Analyst role at Intercontinental Exchange a while back. The process was three rounds: an initial recruiter screen, a technical interview focused on SQL and data analysis, and a final panel with hiring managers.
Questions asked: From what I remember, the technical interview was pretty SQL-heavy. They asked me to write queries involving joins, aggregations, and window functions, then explain why I chose a particular approach. One question involved finding trends in transaction data and identifying outliers.
The case-style questions were more interesting. I was given a scenario where a dashboard metric had suddenly dropped, and they asked me to walk through how I'd investigate it. They kept adding new pieces of information as I answered, so it felt more like a real troubleshooting exercise than a textbook question.
I didn't have a formal take-home assignment, but I was asked to talk through a past analytics project in detail—how I cleaned the data, what assumptions I made, what challenges I ran into, and how I communicated the results.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Intercontinental Exchange
What are the assumptions of linear regression?
| Question | |
|---|---|
| Listing Bookings Aggregation | |
| Why Do You Want to Work With Us | |
| Data Cleaning Experiences | |
| Google Docs Drop | |
| Interpreting Fraud Detection Trends | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Experiment Validity | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Slacking Employees Salaries | |
| User Experience Percentage | |
| First to Six | |
| Button AB Test | |
| 500 Cards | |
| Bagging vs Boosting | |
| Download Facts | |
| Over-Budget Projects | |
| Prime to N | |
| Find the Missing Number | |
| Last Transaction | |
| Department Expenses | |
| Random SQL Sample | |
| Raining in Seattle | |
| Third Purchase | |
| Delivery Estimate Model | |
| Weighted Keys |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with a recruiter to review your background, interest in the Data Analyst role, and overall fit. This stage appears to be a standard first step before moving into technical interviews.
A SQL-heavy interview focused on data analysis skills. Expect to write queries using joins, aggregations, and window functions, explain your approach, and solve a scenario involving transaction trends and outlier detection.
A final round with hiring managers that includes case-style troubleshooting and discussion of past analytics work. You may be asked to investigate a sudden dashboard metric drop step by step and walk through a previous project in detail, including data cleaning, assumptions, challenges, and how you communicated results.