Exl Data and Business Analytics interview typically runs 4 rounds: HR screening, technical screen, case studies, hiring manager. It usually takes a few weeks and is notably structured and analytics-heavy.
$85K
Avg. Base Comp
$109K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
This guide is framed as a Data and Business Analytics interview because the available evidence sits in the broader analytics family rather than a cleanly separate Data Analyst lane.
Our candidates report that Exl is looking for more than someone who can write correct SQL on command. The strongest signal in the experience we saw was the emphasis on explaining the logic behind the query, especially around lag/lead patterns and window functions. That tells us the team cares about analysts who can reason through data structures cleanly and defend their approach, not just produce an answer quickly.
A recurring theme is the company’s preference for people who can move comfortably between technical detail and business context. Multiple candidates described being pushed on probability, statistics, and even basic-to-harder modeling concepts like XGBoost, which suggests the bar extends into practical analytics fluency rather than pure reporting work. We also saw repeated references to project deep-dives, where interviewers wanted specifics beyond resume bullets. In our view, that is the non-obvious make-or-break point here: if you can’t clearly explain what you owned, what assumptions you made, and how your work affected a business problem, the process gets much harder.
The case-style conversations seem to be where Exl separates polished candidates from truly strong ones. Our candidates report that interviewers were less interested in a single perfect final answer and more interested in how they structured the problem and handled ambiguity. That means the people who do best here usually sound methodical, grounded, and comfortable thinking out loud under pressure.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Exl process.
The interview process was pretty structured and leaned heavily on analytics rather than pure coding. I went through four rounds total. The first was an HR screening, which was straightforward and mostly about my background. The second round was the first real technical screen, and that one mixed SQL with probability/statistics plus a couple of business-understanding questions. The SQL portion was the most concrete part of the process: I was asked to work through lag/lead style queries and window functions, so it helped to be comfortable explaining not just the answer but why the query was built that way.
After that came three case studies, which made the process feel much more interactive than a standard interview. They were testing how I approached problems and how I talked through assumptions, not just whether I could get to a final answer. I was also asked about projects from my resume, so I had to be ready to go deeper than the bullet points and explain what I actually did. The final round was with the hiring manager and covered SQL again, my past projects, and team fit. One thing that stood out was that the technical bar wasn’t limited to SQL — there were also questions on basic to harder concepts around XGBoost, so the role seemed to expect some familiarity with modeling as well. Overall it felt challenging but fair, especially if you’re strong in analytics, business context, and can defend your project work clearly. I didn’t get an offer, so I’d say the main takeaway is to prepare for SQL window functions, case-style problem solving, and being able to discuss both statistics and modeling basics confidently.
Prep tip from this candidate
Drill SQL window functions, especially lag/lead patterns, and practice walking through case studies out loud with clear assumptions. Also be ready to explain your resume projects in detail and answer basic XGBoost questions, since modeling came up alongside analytics.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Exl
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Employee Salaries | |
| Bagging vs Boosting | |
| Size of Joins | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Three Zebras | |
| Target Indices | |
| Assumptions of Linear Regression | |
| Duplicate Rows | |
| Type I and II Errors | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| Multicollinearity in Regression | |
| Credit Card Fraud Model | |
| Explaining Linear Regression to Different Audiences | |
| Random Forest from Scratch | |
| Google Earth Storage | |
| Your Strengths and Weaknesses | |
| Branch Sales Pivot | |
| Correlation in Regression | |
| Linear vs Logistic Regression | |
| Booking Regression | |
| Lasso vs Ridge | |
| Random Forest Explanation | |
| Scalped Ticket | |
| Classification and Regression | |
| Success Measurement | |
| Second Ace |
Synthesized from candidate reports. Individual experiences may vary.
An initial screening focused on your background, resume, and general fit for the Data Analyst role. This round was straightforward and served as the first filter before the technical interviews.
The first technical round mixed SQL with probability/statistics and a few business-understanding questions. Candidates should expect concrete SQL work, especially lag/lead queries and window functions, along with explanations of why the query is structured a certain way.
Three case studies followed, making the process more interactive and focused on problem-solving approach rather than just final answers. These rounds tested how you think through assumptions, communicate your reasoning, and apply analytics to business scenarios.
The final round was with the hiring manager and covered SQL again, past projects, and team fit. Expect deeper discussion of your resume projects, plus some modeling basics such as XGBoost concepts.