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.
$81K
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.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Intercontinental Exchange process.
The process started with an HR screening call to confirm basic fit and role expectations. After that cleared, I moved into two technical rounds that felt pretty different from each other. The first technical interview focused on SQL and database concepts—I got questions about dense rank, count(distinct), multi-table joins, and running score calculations. The interviewer was testing my ability to work with window functions and aggregations on real datasets involving student and customer data. The second technical round threw me off a bit because it pivoted to finance domain knowledge. They asked me about types of bonds and their features, which felt less like a typical data analyst question and more about understanding the business context at Intercontinental Exchange. After the technicals, there was a managerial round where my potential manager asked behavioral questions and probed into how I'd approach ambiguous problems and work cross-functionally. The whole thing stretched over several weeks, and honestly, the pacing felt slow—I'd finish a round and then wait a week or more before hearing about the next stage. At the end, after the HR discussion wrapping everything up, I learned they'd gone with a different candidate. It was a long process to go through, and I wish I'd gotten more clarity upfront about timeline and what each round was really evaluating. The technical questions themselves were solid and fair, but the bond domain knowledge caught me off guard—I'd recommend reviewing financial instruments and the business side of the company before interviewing there.
Prep tip from this candidate
Study SQL window functions (dense_rank, running totals, count distinct) on multi-table joins before the technical rounds, and brush up on Intercontinental Exchange's business—especially bond types and features, since domain knowledge came up unexpectedly in the interview process.
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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 | |
| Google Docs Drop | |
| Data Cleaning Experiences | |
| Interpreting Fraud Detection Trends | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Experiment Validity | |
| Monthly Customer Report | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Button AB Test | |
| Slacking Employees Salaries | |
| 500 Cards | |
| User Experience Percentage | |
| First to Six | |
| Compute Deviation | |
| Download Facts | |
| Bagging vs Boosting | |
| Subscription Overlap | |
| Over-Budget Projects | |
| Month Over Month | |
| Maximum Profit | |
| Session Difference | |
| Network Experiment Design | |
| Department Expenses | |
| Raining in Seattle |
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.
