Capgemini Data Engineer candidates report roughly 2-4 rounds centered on SQL, Spark/PySpark, and real pipeline scenarios drawn from their own projects, with cloud tooling that varies by team.
$90K
Avg. Base Comp
$115K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Across Capgemini Data Engineer reports, the clearest pattern is that interviewers repeatedly anchor questions to your own recent project rather than abstract theory. Candidates describe walking through pipelines they actually built and fielding follow-ups on architecture decisions, scalability, cost optimization, and production incidents they resolved. SQL shows up in most accounts, often via live query writing, joins, window functions, aggregations, and performance tuning. Spark and PySpark questions range from direct usage like createOrReplaceTempView and what coalesce does to internals like the Catalyst optimizer and Tungsten. Pipeline design questions push on real decisions: making pipelines idempotent, handling late-arriving data, schema evolution, data quality, and batch versus streaming trade-offs. Cloud and platform tooling varies by team. Some candidates were quizzed on Azure Data Factory and Databricks (including Unity Catalog and mount points), others on GCP or AWS services, Snowflake, or a niche stack like SAP Datasphere, so the tools tested depend on the team you are being considered for.
Round structure also varies. Some candidates had a walk-in aptitude test followed by one technical and one HR round, others four-stage processes with a manager discussion or final panel, and one candidate closed with a prepared presentation. Some candidates also faced live Python coding, including recursion. Behavioral and managerial conversations cover past experience and fit, and the HR stage often turns to salary and next steps. One candidate declined after a compensation offer fell below the minimum stated at the start. Another learned of an experience requirement only after clearing a round. Confirm the salary range and seniority expectations early, and treat resume-based project depth as the most consistent throughline to rehearse.
Synthesized from 20 candidate reports by our editorial team.
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Real interview reports from people who went through the Capgemini process.
I walked in thinking I was prepared. Three rounds total. First was HR, standard stuff. Second round was technical and I felt solid on the SQL questions, even cracked a joke about my debugging nightmares. But then the third round hit me with a live system design scenario I'd never seen before. The interviewer kept asking 'what if it scales?' and I started rambling, hands got sweaty, totally blanked on load balancing for like 30 seconds. Recovered okay but that silence felt like an hour. Got the offer anyway but still think about that moment.
Questions asked: Technical round had me debug a broken REST API call in Java. Then write a SQL join to combine three tables. Asked about microservices vs monoliths, when to use each.
Case round was weird: "Design a system for a pizza chain to predict 30-minute demand." Had to think about data sources, ML vs simple heuristics. Interviewer kept asking "what if the model is wrong?"
Also got: "Tell me about a time you had to push back on a deadline." Classic behavioral.
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Topics based on recent interview experiences.
Featured question at Capgemini
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| SELECTive Wine Connoisseur | |
| Largest Salary by Department | |
| Top 3 Users | |
| Size of Joins | |
| Real-Time Transaction Streaming | |
| Google Maps Improvement | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Transformer Encoder Layer | |
| P-value to a Layman | |
| Digitizing Student Test Scores | |
| RAG Strict Source Control | |
| Count Transactions | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Addressing Data Quality Issues | |
| Popular Products | |
| Merchant Dashboard Design | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Cloud-Agnostic Deployments | |
| Client Solution Pushback | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences | |
| Rolling Bank Transactions |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial filter before live technical discussion. Formats include a walk-in aptitude and reasoning test, resume screening, an English screening call about previous experience, or a short HR or recruiter call. This stage typically decides who advances to a live technical round rather than testing deep data engineering knowledge.
Candidates report live coding or query-writing in this stage, including Python problems such as writing recursive functions, SQL coding questions, joins, and window functions. PySpark and Spark questions range from direct usage (createOrReplaceTempView, what coalesce does) to deeper internals such as the Catalyst optimizer, Tungsten, and Spark optimization. One candidate found the second technical round noticeably harder, with code written on the spot.
Many candidates describe questions centered on their own recent projects: architecture decisions, scalability challenges, cost optimization, business impact, and a production incident they resolved. Scenario questions build on this, such as designing a scalable pipeline for high event volumes, making pipelines idempotent, handling late-arriving data, or managing schema evolution and data quality issues.
Depending on the team, candidates report questions on Azure Data Factory versus Databricks (mount points, ADLS connectivity, Unity Catalog), GCP data engineering, AWS services and lakehouse design, Snowflake, Airflow orchestration, or specialized systems like SAP Datasphere. The toolset tested depends on the team you are being evaluated for, so it may help to confirm the stack in advance.
The closing stage typically covers a manager discussion or final panel, behavioral fit and experience, and a compensation and next-steps conversation. One candidate also prepared and delivered an hour-long presentation. Another reported a final offer below the minimum stated in the first conversation, so confirming salary expectations early may help avoid a late-stage mismatch.
