Infosys Data Engineer candidates report SQL and coding screens, project-heavy technical rounds covering PySpark and ETL, and an HR conversation. Formats and outcomes varied by candidate.
$80K
Avg. Base Comp
$96K
Avg. Total Comp
3-5 rounds
Typical Rounds
Not reported
Process Length
Data Engineer candidates describe an Infosys interview that is broad rather than deep. The common thread is SQL plus your own projects. Candidates were asked to write joins and a second-highest-salary query, and one was asked about Slowly Changing Dimension Type 2 and how to validate it with a query. Interviewers also asked candidates to walk through resume projects, including architecture, ETL responsibilities, incremental loads and identifying duplicate records.
The second theme is Spark and pipeline work. One candidate was asked to write SQL queries and translate them into PySpark. Another discussed foundational Spark concepts alongside ETL and big data testing. A third talked through real-world pipeline challenges, data migration strategies and Power BI. Which tools come up appears to follow the candidate's stack, so know every technology on your resume well enough to explain it.
Coding was lighter but real. Reports include merging two sorted arrays, an anagram problem, a dynamic programming question, and counting unique alphabetic characters with a time and space complexity follow-up. One candidate's online test included a Python problem about designing a billing-system interface from a JSON file. Another had a live round on designing a clickstream analytics schema and ETL pipelines, and said system design was where they felt least prepared.
Outcomes were mixed: some candidates received offers and others did not. Several describe technical rounds followed by an HR conversation, sometimes with a managerial round in between. Practice explaining why your query or pipeline is efficient, not only that it works.
Synthesized from 12 candidate reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
I started with an online assessment in college, and after that cleared I was among 200+ students selected to move forward to the interview rounds. The whole process took about a month from start to finish. There were three rounds total — two technical and one HR.
In the first technical round, they went through my projects and asked me to explain them in detail. They also dug into PySpark functions and gave me some coding problems to solve, mostly in Python and SQL. The questions covered the fundamentals pretty thoroughly — things like how groupby works, renaming columns, and basic data manipulation. They also asked about OOP concepts, CTE clauses in SQL, and some Spark and Scala basics. The coding problems weren't super hard, but they wanted to see if I had a solid grasp of the concepts and could explain my reasoning clearly.
The second technical round focused more on PySpark architecture and deeper concepts like RDDs, map, flatmap, and mappartitions operations. They really wanted to understand if I knew how Spark worked under the hood, not just how to write basic queries. Then came the HR round, which was pretty straightforward — they asked behavioral questions and checked if I'd be a good fit culturally.
One thing I'd say is that the recruiter throughout the process was really supportive and kept me updated, which made the whole experience less stressful. When I got the offer, it took another month before my PD told me what specific role I'd been placed in. The key for me was going in confident, having a solid handle on the basics, and being able to back up everything I said with concrete examples from my projects.
Prep tip from this candidate
Focus heavily on PySpark fundamentals — RDDs, map, flatmap, and mappartitions — and be ready to explain Spark architecture. Brush up on SQL (CTEs, groupby, column renaming) and basic Python coding. Most importantly, be prepared to explain your projects in depth and connect them to the concepts they ask about.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Infosys
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Merge Sorted Lists | |
| Top 3 Users | |
| Find the Missing Number | |
| Retailer Data Warehouse | |
| Size of Joins | |
| Normalize Grades | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| P-value to a Layman | |
| RAG Strict Source Control | |
| Digitizing Student Test Scores | |
| Covariance vs Correlation | |
| Classification and Regression | |
| Swap Variables | |
| Ticket Agent Analysis | |
| Addressing Data Quality Issues | |
| Seller Type Modeling | |
| String Palindromes | |
| Merchant Dashboard Design | |
| Concurrent LLM Serving | |
| Blob Indexing | |
| Safe Deployments | |
| Pipeline Transformation Failures | |
| Check Matching Parentheses | |
| Text Editor With OOP | |
| The Longest Journey | |
| Scalable Data Pipelines | |
| Testing Constraints |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe different entry points. One reports a 30-minute recruiter call with basic SQL questions. Another had an online coding test mixing SQL, Python and cloud multiple-choice questions, and another a live coding test with SQL translated into PySpark. Expect some form of early SQL and Python screen.
Candidates report a mix of coding and SQL: sorted-array merging, anagrams, unique-character counting with complexity analysis, joins and a second-highest-salary query. Interviewers also asked about resume projects, ETL work, duplicate records and Git workflows, so you may need to explain your reasoning aloud.
Later technical conversations reportedly covered PySpark and foundational Spark concepts, ETL and big data testing, data migration scenarios and Power BI. One candidate was asked to design a clickstream analytics schema and discuss ETL pipeline design. Topics appear to follow each candidate's stack and project history.
Several candidates report a final HR conversation covering behavioral questions, culture fit and, for some, compensation expectations. One candidate also described a separate managerial round about past projects and cross-team communication. Interviews may be virtual or in person, according to candidates.
