Capgemini Data Engineer Interview Questions + Guide in 2024
Overview
Capgemini is a global leader in consulting, technology services, and digital transformation, with a rich history of over 55 years. Operating in more than 50 countries and boasting a diverse workforce of 340,000 team members, the company is committed to helping organizations evolve through innovative solutions fueled by AI, cloud, and data.
Stepping into the role of a Data Engineer at Capgemini requires a robust blend of technical prowess and project management skills. Your responsibilities will include developing and optimizing data pipelines, modeling data for transactional and analytical systems, and ensuring data integration across various platforms. Familiarity with essential tools and languages like SQL, Python, PySpark, and cloud technologies like Azure and AWS is essential.
In this guide, we’ll walk you through the interview process for the position, provide insights into commonly asked Capgemini data engineer interview questions, and offer tips to help you prepare effectively. Let’s get started!
Capgemini Data Engineer Interview Process
The interview process usually depends on the role and seniority; however, you can expect the following on a Capgemini data engineer interview:
Recruiter/Hiring Manager Call Screening
If your CV is among the shortlisted few, a recruiter from the Capgemini Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.
Sometimes, the Capgemini data engineer hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.
The whole recruiter call should take about 30 minutes.
Technical Virtual Interview
Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Capgemini Data Engineer role is usually conducted virtually, including video conference and screen sharing. The questions in this one-hour interview stage may revolve around Capgemini’s data systems, ETL pipelines, and SQL queries.
In the case of data engineering roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. In addition, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.
Case studies and similar real-scenario problems may also be assigned depending on the position’s seniority.
Onsite Interview Rounds
After a second recruiter call outlining the next stage, you’ll be invited to attend the on-site interview loop. Multiple interview rounds will be conducted during your day at the Capgemini office, varying with the role. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.
If you were assigned take-home exercises, you may also be invited to a presentation round during the on-site interview for the data engineer role at Capgemini.
What Questions Are Asked in a Capgemini Data Engineer Interview?
Typically, interviews at Capgemini vary by role and team, but commonly, Data Engineer interviews follow a fairly standardized process across these question topics.
1. Write a SQL query to select the 2nd highest salary in the engineering department.
Write an SQL query to select the second-highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.
Note: If more than one person shares the highest salary, the query should select the next highest salary.
Example:
Input:
employees table
| Column | Type |
|---|---|
id |
INTEGER |
first_name |
VARCHAR |
last_name |
VARCHAR |
salary |
INTEGER |
department_id |
INTEGER |
departments table
| Column | Type |
|---|---|
id |
INTEGER |
name |
VARCHAR |
Output:
| Column | Type |
|---|---|
salary |
INTEGER |
2. What are the drawbacks of having student test scores organized in the given layouts?
Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in “messy” datasets.
3. How would you locate a mouse in a 4x4 grid using the fewest scans?
You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. How would you determine the mouse’s location using the fewest number of scans?
4. How would you select Dashers for Doordash deliveries in NYC and Charlotte?
Doordash is launching delivery services in New York City and Charlotte. How would you decide which Dashers to select for these deliveries? Would the selection criteria be the same for both cities?
5. What factors could bias Jetco’s study onboarding times?
According to a study, Jetco, a new airline, has the fastest average boarding times. What factors could have biased this result, and what would you investigate?
6. How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company?
You work at a B2B SAAS company and are interested in testing different subscription pricing levels. How would you design a two-week A/B test to evaluate a pricing increase? How would you determine if the increase is a good business decision?
7. How much should we budget for a $5 coupon initiative in total?
A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.
8. What is the probability of both riders getting the coupon?
A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.
9. What is the probability that only one rider will get the coupon?
A driver using the app picks up two passengers. Determine the probability that only one of them will receive the coupon.
10. What is a confidence interval for a statistic, and why is it useful?
Explain a confidence interval, why it is useful to know, and how to calculate it.
11. What is the probability that item X would be found on Amazon’s website?
Amazon has a warehouse system with items located at different distribution centers. Given the probability that item X is available at warehouses A (0.6) and B (0.8), calculate the probability that item X would be found on Amazon’s website.
12. Is this a fair coin?
You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.
13. What are time series models, and why do we need them?
Describe what time series models are and explain why they are necessary when simpler regression models exist.
14. How would you explain linear regression to a child, a college student, and a mathematician?
Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician, tailoring your explanations to their understanding levels.
15. How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment?
As a data scientist at a bank, determine if a decision tree algorithm is appropriate for predicting loan repayment. Evaluate the model’s performance before and after deployment.
16. How would you justify using a neural network model and explain its predictions to non-technical stakeholders?
If tasked with building a neural network model to solve a business problem, justify its complexity and explain the predictions to non-technical stakeholders.
17. How does random forest generate the forest, and why use it over logistic regression?
Describe how a random forest generates its ensemble of trees and explain why it might be preferred over logistic regression for certain problems.
18. What are the key differences between classification models and regression models?
Identify and explain the main differences between classification models and regression models.
How to Prepare for a Data Engineer Interview at Capgemini
You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Capgemini interview include:
Master Key Technologies: Capgemini heavily emphasizes technical knowledge, particularly around tools like PySpark, Python, SQL, and cloud platforms like Azure. Ensure you’re well-versed in these technologies.
Scenario-Based Questions: Be prepared for in-depth scenario-based questions that test not just your technical aptitude but also your problem-solving skills and ability to handle complex data engineering projects.
Soft Skills Matter: Apart from technical skills, Capgemini emphasizes communication abilities and cultural fit. Be ready to answer questions on teamwork, project challenges, and how you navigate work-life balance.
FAQs
What is the average salary for a Data Engineer at Capgemini?
Average Base Salary
Average Total Compensation
What is the role of a Data Engineer at Capgemini?
A Data Engineer at Capgemini is responsible for developing, optimizing, and maintaining data pipelines and architectures. This involves working with tools like Azure Data Factory, PySpark, and SQL. The role also includes collaborating with data scientists and analysts, ensuring robust and scalable data systems, and automating tasks using tools like Azure DevOps.
What is the company culture like at Capgemini?
Capgemini is known for its inclusive and collaborative culture. The company values diversity, continuous learning, and work-life balance. You’ll be part of a global community where you can shape your career, innovate with cutting-edge technology, and contribute to impactful projects.
Conclusion
Capgemini is praised for its well-organized approach, quick feedback loops, and supportive recruitment staff. Aspiring candidates should be well-versed in tools like Python, PySpark, Azure Data Factory, and SQL, and be prepared for scenario-based questions.
If you want more insights about the company, check out our main Capgemini Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as data engineer, where you can learn more about Capgemini’s interview process for different positions.
You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.
Good luck with your interview!