EPAM Data Analyst candidates report an HR conversation, a hands-on technical interview in SQL, Python and Power BI, and in one case a project manager discussion. Here is what to practice and what to expect.
$100K
Avg. Base Comp
$130K
Avg. Total Comp
Not reported
Typical Rounds
3-4 weeks
Process Length
Candidates describing EPAM Data Analyst interviews most often point to SQL, Python and Power BI/DAX as the core of the technical conversation. One 1.5-hour technical interview opened with a review of the candidate's experience, then moved to live SQL with four questions covering aggregation, window functions and joins. The interviewer reportedly cared more about logic and syntax knowledge than fully working code. Python questions were described as easy and covered DataFrames and writing a function. The Power BI portion asked about DAX and calculated columns, and the candidate was shown a dashboard and asked what they would change to make it more readable.
Another candidate described a similar mix: Python lambda functions and list comprehensions, SQL window functions, UNION versus UNION ALL, and NVL, followed by scenario-based analysis questions and a discussion with a project manager about soft skills and team fit. A third reported multiple rounds over nearly a month, with SQL, Power BI, DAX and practical reporting scenarios alongside managerial and project discussions.
Communication is the other recurring theme. Candidates mention rescheduling, conflicting status updates and silence after the technical stage. Several reports ended without an offer, so prepare to explain your reasoning aloud and expect the timeline to stretch. Senior-level reports are mixed in here: one asked about materialized versus regular views, and another centered on a medium SQL window-function exercise.
Synthesized from 5 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Epam Systems process.
I went through multiple interview rounds over nearly a month for a Data Analyst role, and the process ended up feeling more drawn out than difficult. The technical discussions were solid and mostly centered on SQL, Power BI, DAX, and real-time scenarios, so I spent most of my time talking through how I would handle reporting and analysis work in practice rather than doing anything overly algorithmic. There were also managerial and project discussions mixed in, which made it feel like they were trying to understand both my hands-on skills and how I’d fit into the team.
What stood out most was the lack of coordination. I got conflicting updates about my interview status and even about location preference, which made the process confusing and added unnecessary delays. The interviews themselves were fine, but the communication around them was not great. In the end, I did not get an offer. My main takeaway is to be ready for practical SQL and BI questions, especially around Power BI and DAX, and to stay patient because the process may take longer than expected.
Prep tip from this candidate
Focus on SQL, Power BI, and DAX, especially how you’d apply them to real reporting scenarios. Be ready for project and managerial discussions too, since the process wasn’t just technical.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Epam Systems
Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads
| Question | |
|---|---|
| Classification and Regression | |
| SageMaker Deployment Architecture | |
| Weighted Average Sales | |
| Best DAU | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| First to Six | |
| Monthly Customer Report | |
| Lowest Paid | |
| Experiment Validity | |
| Download Facts | |
| Prime to N | |
| First Touch Attribution | |
| Top 3 Users | |
| Random SQL Sample | |
| Compute Deviation | |
| Find the Missing Number | |
| Largest Salary by Department | |
| Button AB Test | |
| Employee Salaries (ETL Error) | |
| 500 Cards | |
| Paired Products | |
| Raining in Seattle |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report the process typically begins with an HR or recruiter contact, sometimes through LinkedIn. One candidate described this screen as focused on background, experience and standard behavioral questions. Be ready to summarize your analytics projects and the tools you have used, especially SQL and Power BI.
This stage appears in the reports as a hands-on technical interview. One lasted about 1.5 hours and included four SQL questions on aggregations, window functions and joins, plus easy Python DataFrame and function questions. Another candidate reported lambda functions, list comprehensions, UNION versus UNION ALL and NVL. Interviewers may care more about your logic than perfectly working code.
Candidates report Power BI and DAX questions, including calculated columns. One was shown a dashboard and asked what they would change to make it more readable. Practice explaining how you would turn an analysis into a clear report, and how you would apply DAX to realistic reporting scenarios.
Candidates describe situational or scenario-based questions, and one reports a discussion with a project manager covering soft skills, cultural fit and how they would work within the team. Another mentions managerial and project conversations. Prepare examples of past projects, and note that follow-up may be slow or inconsistent, with some candidates reporting conflicting status updates or no reply.
