Infosys Data Engineer interview typically runs 3 rounds: virtual technical, in-person technical, and HR. It usually takes a few weeks, and the process is broad and resume-driven.
$81K
Avg. Base Comp
$118K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Infosys is less interested in a single specialty than in whether you can move cleanly across the full data-engineering stack. A recurring theme is the mix of SQL depth, coding fluency, and project-level explanation: one candidate had to jump from array problems into joins and ETL scenarios, while another was pressed on Git, normalization, ACID, and SCD Type 2. The pattern is clear — they want people who can connect the dots between theory and the systems they’ve actually built.
We’ve also seen that the interviewers lean heavily on your resume as a source of follow-up questions. Multiple candidates said the conversation quickly became tied to their current stack, prior projects, incremental loads, duplicate handling, Spark, dbt, Snowflake, or testing. That means the real signal is not just whether you know the tool names, but whether you can explain why a design choice was made and how you validated it. Short, confident answers that stay at the surface tend to get pushed on.
Another non-obvious pattern is the emphasis on explanation over completion. Even when the coding problem was manageable, candidates were asked to optimize, justify complexity, or walk through edge cases. In other words, Infosys seems to reward candidates who can defend their approach in plain language and tie it back to production data work. The strongest experiences we saw came from candidates who treated each question as a chance to show practical judgment, not just correctness.
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 | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| P-value to a Layman | |
| Digitizing Student Test Scores | |
| Normalize Grades | |
| Covariance vs Correlation | |
| Classification and Regression | |
| Ticket Agent Analysis | |
| Swap Variables | |
| String Palindromes | |
| Seller Type Modeling | |
| Concurrent LLM Serving | |
| Pipeline Transformation Failures | |
| Check Matching Parentheses | |
| Text Editor With OOP | |
| The Longest Journey | |
| Testing Constraints | |
| Cloud-Agnostic Deployments | |
| Azure Kubernetes Infrastructure | |
| Safe Deployments | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Student Tests |
Synthesized from candidate reports. Individual experiences may vary.
The process typically starts with a recruiter-led conversation to confirm your background, availability, and fit for the Data Engineer role. In at least one experience, the recruiter also helped manage a technical connection issue and kept the process moving calmly.
The first technical round is broad and can begin immediately with coding, often around arrays or strings, followed by SQL questions and database fundamentals. Candidates were asked to explain their approach, optimize solutions, and discuss topics like joins, normalization, ACID properties, Git workflows, and complexity analysis.
Interviewers spend significant time on your resume and past projects, especially ETL work, architecture, incremental loads, duplicate handling, and testing. Depending on your stack, the discussion may include Python, PySpark, dbt, Snowflake, Spark concepts, Control-M, and scenario-based questions tied to real project experience.
Some candidates had a second technical round, either virtual or in person at the office, with more coding and SQL plus follow-up questions on data engineering concepts. This round can also include DSA problems such as anagrams or dynamic programming, along with practical questions about cursor behavior and ETL scenarios.
The final stage is an HR discussion focused on personality, working style, and overall fit. Candidates also reported questions about their background and how they handle day-to-day responsibilities, after which the final hiring decision is communicated.