Wipro Data Engineer interviews, as candidates describe them, combine SQL, Python, PySpark and Azure Data Factory questions with detailed project walk-throughs and an HR discussion. Some candidates also report an online assessment.
$104K
Avg. Base Comp
$149K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe Wipro's Data Engineer interviews as manageable but dependent on the project and client. The common thread across the five reports is that interviewers want you to explain real project work in detail, then back it up with SQL, Python and, depending on the team, PySpark or Azure tooling.
One candidate described an online assessment with timed aptitude sections and two coding problems on strings, arrays and loops, followed by a voice and communication test. Another described L1 and L2 technical rounds. Those covered SQL coding, the full data flow of a project, and a Treasure Data workflow. A third candidate went through a virtual technical round on Python, SQL, PL/SQL and database scaling, then a face-to-face round and an HR conversation. That candidate also expects further client-facing technical rounds after joining.
Tool depth varied by report. One candidate highlighted Spark optimization topics worth preparing: repartition versus coalesce, reduceByKey versus groupByKey, caching, salting and reading an execution plan. Another was asked about Azure Data Factory migration from on-premises to the cloud, integration runtimes, blob storage versus ADLS, and views versus materialized views. A candidate who did not receive an offer reported Python generator and PySpark aggregation coding, plus joins and data warehousing questions, and said interviewers valued the reasoning process.
For preparation, rehearse one project end to end, practice SQL scenarios such as second-highest salary, and review the stack named in your job description. HR conversations in these reports touched on salary, relocation or service terms, and how long the outcome took. One candidate waited about a week.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Wipro process.
I walked in expecting a standard technical interview, but Wipro's process felt more conversational than I anticipated. The panel members were genuinely interested in my background and what I'd actually done, rather than just firing abstract questions at me. They grounded everything in my resume—asking me to walk through specific projects I'd listed and diving into the technical decisions I'd made there. It was refreshing because it meant I could talk about real work, not just memorized algorithms.
The technical portion covered a mix of SQL and Python. I got questions on joins and data warehousing concepts early on, which felt like a way to gauge my foundational understanding. What surprised me was how much they emphasized the approach over the answer itself. One of the interviewers explicitly said they care more about how you think through a problem than whether you nail it perfectly the first time. That shift in mindset actually helped me relax—I started explaining my reasoning out loud instead of just coding in silence.
The tougher part came when they asked me to write Python code, including a generator problem and later an aggregation task using PySpark dataframes. The PySpark question specifically asked me to solve an aggregation challenge, and that's where I felt the time pressure. They gave me a problem, I had to write working code, and explain my logic. By that point I was a few rounds in and a bit fatigued, which probably didn't help. The whole process lasted several hours across multiple rounds, and mentally it was more taxing than I expected.
Prep tip from this candidate
Focus heavily on PySpark DataFrame operations—specifically aggregation functions—and be ready to code them live. Also, make sure you can articulate your approach to SQL joins and data warehousing patterns before diving into the answer, since the interviewers are evaluating your reasoning process as much as your final solution.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Wipro
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Prime to N | |
| Largest Salary by Department | |
| Find the Missing Number | |
| Size of Joins | |
| Groups of Anagrams | |
| Cyclic Detection | |
| Repeated Category Purchase | |
| Valid Anagram | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Flatten N-Dimensional Array to 1D Array | |
| Duplicate Rows | |
| Swap Variables | |
| Assumptions of Linear Regression | |
| Seller Type Modeling | |
| String Palindromes | |
| Messenger Service Design | |
| Sales vs Revenue | |
| Blob Indexing | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| International e-Commerce Warehouse | |
| Expansion Plan | |
| Text Editor With OOP | |
| Cloud-Agnostic Deployments | |
| Last Element of a Singly Linked List | |
| Client Solution Pushback | |
| Relational Migration | |
| Why Do You Want to Work With Us |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a timed online assessment with quantitative, logical and verbal sections, then two coding problems on strings, arrays and loops in a language of choice. Another mentions a similar aptitude and technical MCQ pattern. Not every report mentions this stage, so it may depend on the hiring route.
One candidate describes a voice assessment where they read sentences aloud, responded to audio clips and completed grammar and pronunciation tasks. It tested professional communication rather than data engineering. Other reports do not mention it, so treat it as route-dependent.
Candidates report one to two technical rounds, online or in person. Topics include SQL scenarios, Python, PySpark aggregations, Spark optimization, Azure Data Factory, ETL optimization and walk-throughs of real project architecture. One report involved a client representative in the second round. Depth typically follows the resume and project stack.
Candidates report a final HR or leadership conversation. It covered self-introduction, adaptability, salary, relocation, service agreement terms and joining timing. One candidate says the outcome took about a week. Another expects further client-allocation technical rounds after accepting the offer, which may vary by project.
