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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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.
