Infosys Data Engineer interviews reported in 2026 combine resume-led technical discussion with SQL, coding, ETL, and behavioral questions. One candidate described a virtual technical round, an onsite round, and HR discussion.
$105K
Avg. Base Comp
$127K
Avg. Total Comp
3 rounds
Typical Rounds
17 days
Process Length
Infosys Data Engineer candidates describe a broad technical conversation rather than a single narrow assessment. SQL and practical project reasoning recur across the reports. Be ready to write joins, address duplicate records and second-highest-salary queries, and explain why a query or validation approach works. One candidate also encountered normalization, ACID properties, SCD Type 2 validation, and incremental-load questions, while another reported SQL joins alongside ETL, big-data testing, and Spark fundamentals.
Coding can appear early and requires explanation beyond a working answer. Reported prompts included merging two sorted arrays, an anagram problem, dynamic programming, and counting unique alphabetic characters with time- and space-complexity follow-up. Review common array and string techniques, then practice narrating trade-offs as you solve.
Your resume is part of the technical evaluation. Candidates were asked to discuss architecture, constraints, responsibilities, project stack, and ETL scenarios, so prepare concrete examples that connect your work to the tools you list. Behavioral questions about working style were also reported. One account describes a virtual first round, an in-person round, and an HR discussion; formats may vary because the evidence is limited.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
The interview opened with a medium-level array problem, so I had to get into coding immediately. I was asked to merge two sorted arrays and explain my approach rather than just produce a working solution. From there, the discussion moved into SQL, with several queries involving joins and follow-up questions about the reasoning behind them. The technical portion then returned to DSA for two more problems, including an anagram-related question and a dynamic programming problem. The difficulty felt manageable but broad: the challenge was switching quickly between algorithms, SQL, and practical data engineering topics.
The process included a virtual first round, followed by an in-person round at the office and an HR discussion. During the technical conversations, the interviewers also spent time on my resume, previous projects, day-to-day responsibilities, and scenarios I had handled in ETL work. They wanted to understand how I applied concepts in real projects, not just whether I could answer isolated questions. The data engineering discussion covered SQL, ETL, big data testing, and foundational Spark concepts. There was also a database question about what happens when a cursor is opened and fetched. Depending on the project stack, the conversation can touch Python, PySpark, dbt, or Snowflake, but my interview leaned most heavily toward SQL and project scenarios.
The overall experience was positive. I had a technical issue while connecting to one of the sessions, but the recruiter handled it calmly and guided me through the process. The interviewers were respectful and gave me room to explain my thinking and discuss my experience. I ultimately received and accepted the offer. My main takeaway is that the interview is not only about solving coding questions: be ready to move between array and string problems, SQL joins, ETL scenarios, and detailed explanations of your own projects without much transition.
Prep tip from this candidate
Practice merging sorted arrays, anagram problems, dynamic programming, and writing SQL joins under interview conditions. Also prepare concrete explanations of your ETL projects, day-to-day responsibilities, Spark fundamentals, big data testing, and database cursor open/fetch behavior.
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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 | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| P-value to a Layman | |
| Digitizing Student Test Scores | |
| Covariance vs Correlation | |
| Classification and Regression | |
| Swap Variables | |
| Ticket Agent Analysis | |
| Addressing Data Quality Issues | |
| Seller Type Modeling | |
| String Palindromes | |
| Blob Indexing | |
| Concurrent LLM Serving | |
| Merchant Dashboard Design | |
| Pipeline Transformation Failures | |
| Text Editor With OOP | |
| Check Matching Parentheses | |
| The Longest Journey | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Cloud-Agnostic Deployments | |
| Azure Kubernetes Infrastructure |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report questions about background, current technologies, project architecture, constraints, ETL scenarios, and day-to-day responsibilities. Prepare to explain how you applied the tools on your resume and the reasoning behind implementation choices.
Candidates report array and string coding tasks, including merge-sorted-arrays, anagrams, and unique-character counting, with complexity follow-ups. SQL questions included joins, duplicate records, and second-highest salary; some reports also mention database concepts and SCD Type 2 validation.
One candidate described a virtual technical round followed by an in-person round and HR discussion. Reported later conversation topics included ETL, Spark or big-data testing, project scenarios, and behavioral questions about personality and working style; the exact format may vary.
