Exl Data Analyst candidates report a four-round process featuring SQL, probability and statistics, case studies, resume-project discussion, and a final hiring-manager conversation.
$91K
Avg. Base Comp
$111K
Avg. Total Comp
4 rounds
Typical Rounds
2-4 weeks
Process Length
The available Exl Data Analyst report describes a structured, analytics-led process rather than a pure coding screen. One candidate reported four rounds: an initial HR conversation, a technical screen, case-study work, and a final discussion with the hiring manager. SQL window functions, especially lag/lead patterns, were a concrete technical focus, alongside probability, statistics, and business-understanding questions.
Prepare to explain your query logic as well as the result. The candidate was asked to discuss why a SQL query was constructed a certain way, so practice narrating assumptions, joins, partitions, ordering, and edge cases. Case studies were described as interactive and centered on how the candidate approached a problem and communicated assumptions. A Netflix-style recommendation prompt also appeared in another supplied report, making it sensible to rehearse a clear, business-aware approach to open-ended analytics scenarios.
Resume projects mattered in both reports. Be ready to go beyond project bullets: explain the problem, your contribution, choices made, and outcomes. The Data Analyst report also mentions XGBoost concepts, so review the modeling basics you can discuss accurately in an analytics context. The sample is limited, but it consistently points to a blend of SQL fluency, structured case communication, and credible project discussion.
Synthesized from 1 candidate report 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 | |
| Type-ahead Search | |
| Three Zebras | |
| Hurdles In Data Projects | |
| Target Indices | |
| Assumptions of Linear Regression | |
| Duplicate Rows | |
| RAG Strict Source Control | |
| Type I and II Errors | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Multicollinearity in Regression | |
| Overfit Avoidance | |
| 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 | |
| Scaling Up Recommender | |
| Designing a Fraud Detection System | |
| Random SQL Sample | |
| Booking Regression | |
| Lasso vs Ridge |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported an initial HR screening that was straightforward and focused mainly on background. Prepare a concise account of your experience and the kind of analytics work you have done; the report does not provide further detail on this conversation.
Candidates report a technical screen mixing SQL with probability/statistics and business-understanding questions. SQL included lag/lead-style window-function work, with an expectation that the candidate could explain why the query was built that way, not only produce an answer.
One report describes three case studies after the technical screen, while another report mentions a Netflix recommendation case. Candidates may be assessed on how they frame the problem, state assumptions, and communicate an approach rather than on a single final answer alone.
The reported final round with a hiring manager revisited SQL, past projects, and team fit. The same report mentions basic through harder XGBoost concepts, so candidates may benefit from being able to explain relevant modeling fundamentals and defend the choices in their project work.
