Myntra Data Analyst candidates report SQL-heavy technical rounds, plus Excel and pandas checks, business case studies and a hiring manager conversation. Here is how to prepare.
$17K
Avg. Base Comp
$20K
Avg. Total Comp
3-4 rounds
Typical Rounds
Not reported
Process Length
Both candidates who described the Myntra Data Analyst process point to SQL as the foundation. One reports window functions such as ROW_NUMBER() and RANK(), nth-highest-value problems, rolling averages and self joins, framed around e-commerce tasks like finding top-selling products and repeat customers. The other also reports window functions, along with query optimization and schema setup work.
Outside SQL, the reports mention light tools checks: a pandas question in one account, and Excel index/match plus pandas basics in the other. Expect to explain your reasoning as you code, not only to produce a working query.
The case study rounds are where the reports differ most in outcome. One candidate describes supply chain and logistics problems, such as improving the order-to-delivery cycle and investigating a customer who received the wrong product. That candidate felt they spent too long on data collection and not enough on hypothesizing root causes, and did not receive an offer. The other describes business cases that translate product questions into analysis, and received an offer. A sensible approach is to define the metric first, propose likely drivers, then say how you would test each one.
The counts of rounds differ between the two accounts, with one describing an online assessment, a technical round, a case study and a hiring manager conversation, and the other describing a screening call followed by four rounds. Treat the sequence as variable. The hiring manager conversation in one account covered background, motivation for joining and start date, so prepare a concise story about past analytical work.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Myntra process.
I got called for a screening and the interview was scheduled for the next day—the whole process moved quickly. The first technical round focused heavily on SQL, which I learned the hard way is absolutely critical for this role at Myntra. They asked me about window functions, and I was also tested on index/match in Excel and pandas basics. The SQL questions weren't just syntax—they wanted to see how I'd approach optimizing queries and working with data structures, so I made sure to explain my reasoning as I coded.
After the technical screen, the process continued with three more rounds. One was another technical round involving schema setup and more SQL work, which reinforced how central database knowledge is here. The other two rounds were less traditional coding and more about problem-solving and business case studies. The interviewers were genuinely smart and put me at ease throughout, and the HR team kept everything on schedule, which I appreciated. They presented realistic scenarios and wanted to see how I'd think through a data problem from a product perspective, not just write queries in isolation. My biggest takeaway: if you're interviewing here, SQL proficiency isn't optional—it's the foundation. The case study rounds tested whether I could translate business questions into analytical approaches, so having both technical depth and the ability to communicate findings clearly made a real difference.
Prep tip from this candidate
SQL is non-negotiable for Myntra—drill window functions, query optimization, and schema design specifically. Be ready to also handle Excel (index/match), basic pandas, and business case studies where you translate product questions into data analysis.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Myntra
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| Question | |
|---|---|
| Declining Applicants | |
| International e-Commerce Warehouse | |
| Simple Explanations | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Compute Deviation | |
| Experiment Validity | |
| Download Facts | |
| Random SQL Sample | |
| Subscription Overlap | |
| Month Over Month | |
| Button AB Test | |
| Prime to N | |
| Paired Products | |
| Upsell Transactions | |
| Top 3 Users | |
| Swipe Precision | |
| P-value to a Layman | |
| Longest Streak Users | |
| Bank Fraud Model | |
| Encoding Categorical Features | |
| Rolling Average Steps | |
| Identifying User Sessions | |
| Exam Scores |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report different openings. One describes an online assessment, while another describes a screening call with the interview scheduled the next day. Typically you should be ready to start with SQL on short notice, so keep your query fundamentals fresh before applying.
Candidates report a heavily SQL-focused technical round: window functions such as ROW_NUMBER() and RANK(), nth highest value, rolling averages, self joins, and e-commerce queries. One also reports Excel index/match and pandas basics, and another technical round with schema setup. Explain your reasoning as you work.
Candidates report case studies rather than coding, including order-to-delivery improvement, investigating a wrong-product delivery, and translating product questions into analysis. You may do better by defining metrics, hypothesizing root causes and describing how you would test each, rather than listing data sources.
One candidate reports a final, more conversational round with the hiring manager covering background, motivation for joining Myntra, recent analytical work and start date. Another describes additional rounds that were less coding-focused and more about problem solving. Prepare a concise account of past projects and your impact.
