Cognizant's Data Engineer interviews typically combine a communication/aptitude screen, a cluster-based coding and SQL assessment, and resume-driven technical rounds covering SQL, Python, and, for some projects, PySpark and Azure tools; stages vary by program.
$111K
Avg. Base Comp
$132K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Cognizant's Data Engineer interviews tend to follow a recognizable shape, though the mix of stages depends on the hiring program and the cluster you're assigned. Several candidates describe an early online communication test checking English proficiency, sometimes alongside an aptitude round on percentages, profit and loss, ages, and probability. A cluster-based technical assessment usually follows, combining Python coding, SQL queries, and multiple-choice questions. One candidate's version ran about 120 minutes, and the content shifted by cluster, from OOP, map/filter/reduce, NumPy, and Pandas to cloud basics and DSA.
SQL is the clearest technical throughline. Candidates were asked to write queries such as finding the second-highest salary and to explain SQL versus NoSQL. In one walk-in process, a candidate also had to translate SQL logic into PySpark. Platform-specific depth depends on the project. One candidate faced IICS, Unix, and APIs, while another fielded Azure Data Factory, Databricks, ETL versus ELT, data warehouse concepts, and performance tuning. Scenario questions test whether you can reason end to end rather than recite syntax. Examples include troubleshooting a suspended job and explaining how data flows from source systems into a warehouse.
Expect interviewers to spend real time on your resume and projects, asking about individual contributions and the reasoning behind technical decisions. One candidate was also asked to explain the logic of several live coding answers in detail. Final technical and HR conversations may cover practical questions like night-shift willingness, location preference, and internal referrals, so decide your answers beforehand. Timelines vary, and one candidate waited about a month between the assessments and the final interview.
Synthesized from 13 candidate reports by our editorial team.
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Real interview reports from people who went through the Cognizant process.
This was a Returnship program, and I was told upfront that the interviewer was looking for Data Engineer experience with Pyspark, Databricks, big data analytics, and Hadoop. No formal job description was shared—just word of mouth from HR about the tech stack they needed. The process had multiple layers: a communication assessment online, followed by a technical round with Python, SQL coding, and MCQs, then a final interview focused on projects and domain interests. In that final round, I got hit with 3–4 coding questions and was asked to explain my code's logic thoroughly. Most questions pulled directly from my resume or tested Python and SQL concepts—decorators, APIs, HTTP status codes. The technical interview itself ran about 90 minutes and was heavy on resume walkthrough and follow-up questions about college activities and past work. The vibe was methodical: they wanted to see if you could problem-solve on the fly and articulate your reasoning.
Prep tip from this candidate
If interviewing for a Returnship Data Engineer role, come prepared to deep-dive on Pyspark, Databricks, and ETL pipelines—those are the core asks. Be ready to explain every project on your resume in detail and walk through your code logic step-by-step, not just the end result.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Cognizant
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Prime to N | |
| Largest Salary by Department | |
| Size of Joins | |
| Random Forest Explanation | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Move Zeros Back | |
| ATM Robbery | |
| Slow SQL Query | |
| Swap Variables | |
| Dijkstra implementation | |
| Finding the Maximum Number in a List | |
| Find the Missing Element | |
| String Palindromes | |
| Triangle as Binary Array | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Impossibly Iterative Fibonacci | |
| Fixed-Length Arrays: Deletion | |
| Text Editor With OOP | |
| Fixed Length Arrays: Addition | |
| Search Increase | |
| Azure Kubernetes Infrastructure | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Reddit-like Notifications | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Closest SAT Scores |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report an early online communication test covering speaking, listening, and reading in English. One candidate also had a separate aptitude round on percentages, profit/loss, ages, and probability at roughly a 10th-11th grade level. This stage is described as straightforward if your English fundamentals are solid.
Candidates describe a timed assessment combining coding problems, SQL queries, and multiple-choice questions whose content depends on your assigned cluster; one candidate noted roughly 120 minutes. Reported examples include two Python problems, such as reversing a string or finding the second-highest element in an array, and two SQL queries. Candidates also mention around 20 MCQs on OOP, map/filter/reduce, NumPy/Pandas, or cloud fundamentals.
In technical interviews, candidates wrote SQL queries such as finding the second-highest salary. In one walk-in process, a candidate then translated that logic into PySpark. Other reported topics include Azure Data Factory, Databricks, ETL versus ELT, data warehouse concepts, performance tuning, IICS, Unix, and APIs. Scenario questions, such as troubleshooting a suspended job, also came up.
Most candidates mention questions built around their resume and past projects, covering individual contributions, technologies used, and the reasoning behind technical decisions. One candidate faced 3-4 coding questions in this round and had to explain the logic thoroughly. Another reported managerial questions and the possibility of being considered for multiple projects.
The final Technical and HR interview may be held offline or online via Superset. Alongside resume-based discussion, candidates report practical questions about willingness to work night shifts, location preferences, and whether you have internal recommendations.
