Barclays Data Engineer Interview Questions + Guide in 2024

Barclays, one of the world’s largest and most respected financial institutions, has been a cornerstone of the finance industry since its founding in 1690. Known for its rich legacy of success, quality, and innovation, Barclays has been instrumental in shaping the financial landscape by offering cutting-edge financial and digital solutions. With a significant and growing presence in the USA, Barclays provides a breadth of opportunities for career growth and development.

If you’re aiming to join a pioneering financial institution like Barclays, this guide is tailored for you. We’ll navigate you through the interview process, highlight commonly asked Barclays data engineer interview questions, and share insights to help you succeed. Let’s dive in!

The interview process usually depends on the role and seniority, however, you can expect the following on a Barclays data engineer interview:

Recruiter/Hiring Manager Call Screening

If your application stands out, a recruiter from Barclays will reach out to schedule a phone screening. This initial conversation typically lasts around 30 minutes and includes verifying your background, discussing your experiences, and assessing your interest in Barclays.

Expect behavioral questions, and be prepared to discuss your technical background in data warehousing, system design, or other relevant areas. The recruiter might also touch upon logistics like your availability for interviews and work preferences (e.g., hybrid work setup).

Technical Virtual Interview

Successfully passing the phone screening will lead to a technical virtual interview. This stage usually involves video conferencing and screen sharing, lasting about an hour. You will be evaluated on various technical aspects, including: - Your familiarity with data warehousing, ETL pipelines, and related technologies. - Coding skills in languages such as Python and SQL. - Understanding of ML models and data architecture.

Specific questions might revolve around dimensional modeling, cloud solutions, and data analytics tools. Depending on the role, you might also be given take-home assignments or live problem-solving scenarios covering real-world use cases.

Onsite Interview Rounds

If you excel in the technical screening, you’ll proceed to the onsite interview rounds, which are extensive and involve multiple sessions. These typically include the following: - Detailed technical interviews focused on your programming skills, system design ability, and experience with large-scale data warehouses. - Behavioral interviews to understand your soft skills, cultural fit, and alignment with Barclays’ values.

You might be asked to present any take-home assignments or discuss case studies. The onsite interviews will also feature opportunities to interact with future team members and gain insights into Barclays’ work culture.

Typically, interviews at Barclays vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

1. Create a function sorting to sort a list of strings in ascending alphabetical order without using the built-in sorted function.

Given a list of strings, write a function, sorting from scratch to sort the list in ascending alphabetical order. Return the new sorted list rather than modify the list in place. Aim for a solution with (O(n \log n)) complexity.

2. How would you design a function to detect anomalies in univariate and bivariate datasets?

If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

3. What are the drawbacks of the given student test score data layouts, and how would you reformat them?

Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in “messy” datasets.

4. What is the expected churn rate in March for customers who bought a subscription since January 1st?

You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition each month and a 20% month-over-month decrease in churn, calculate the expected churn rate in March for all customers who bought the product since January 1st.

5. How would you explain a p-value to a non-technical person?

Describe what a p-value is in simple terms for someone who is not familiar with technical or statistical concepts.

6. What are Z and t-tests, and when should you use each?

Explain what Z and t-tests are, their uses, the differences between them, and when to use one over the other.

7. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

8. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and describe scenarios where bagging is preferred over boosting. Provide examples of the tradeoffs, such as variance reduction in bagging and bias reduction in boosting.

9. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model developed by the co-worker for loan approval.
2. Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.
3. List the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

10. What’s the difference between Lasso and Ridge Regression?

Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

11. What are the key differences between classification models and regression models?

Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

12. What metrics would you use to evaluate the value of marketing channels?

Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine each channel’s value.

13. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

14. How would you investigate if a redesigned email campaign led to increased conversion rates?

Analyze a scenario where a new email journey increased conversion rates from 40% to 43%, considering previous fluctuations. Determine if the redesign caused the increase or if other factors were involved.

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 Barclays data engineer interview include:

• Understand Barclays’ Core Technologies: Familiarize yourself with the key technologies Barclays leverages, such as IBM Netezza, AWS cloud, Spark, Informatica, Python, and SQL. Demonstrating deep technical knowledge in these areas can give you a significant edge.
• Be Ready for Behavioral Questions: Barclays places a strong emphasis on cultural fit and adaptability. Prepare to answer behavioral questions that explore your past experiences, problem-solving approaches, and ability to work within a team.
• Highlight Real-World Applications: Showcase your experience by discussing projects where you’ve designed and implemented data solutions. Emphasize your role in developing ETL processes, building data pipelines, and supporting large-scale data architectures. Be specific about your contributions and the impact of your work.

What is the average salary for a Data Engineer at Barclays?

What skills are essential for a Data Engineer role at Barclays?

Key skills include expertise in data warehousing, ETL processes, and data architecture. Proficiency in tools and technologies like SQL, Python, AWS, Hadoop, Spark, Scala, and Kafka is highly desirable. Dimensional modeling and system design experience also play a significant role.

What is the working environment like at Barclays?

Barclays offers a hybrid working experience, blending the positives of onsite collaboration with the flexibility of working from home. The Whippany, NJ office provides state-of-the-art facilities like pool tables, cafes, and gyms to create a vibrant workplace.

What are the growth opportunities for a Data Engineer at Barclays?

Barclays, being a legacy institution in finance, provides ample opportunities for career growth, working on high-impact projects like regulatory reporting systems and market post-trade functions. Career development is supported through a structured approach involving cross-functional collaborations.

How does Barclays support work-life balance for Data Engineers?

Barclays is committed to offering flexible working arrangements. Discussions about specific work patterns can be had with the hiring manager. The company promotes a culture that balances professional and personal lives, aiding in a healthy work-life integration.

As financial services continue to dynamically evolve, Barclays is searching for proficient and innovative Data Engineers to join their ranks and contribute to their legacy of success and innovation. By honing your technical and behavioral expertise and leveraging the insights derived from previous interview experiences and job role expectations, you’ll be well-positioned to ace your interview.

If you want more insights about the company, check out our main Barclays Interview Guide, where we have covered many interview questions that could be asked. Additionally, explore our interview guides for other roles such as software engineer and data analyst to learn more about Barclays’ interview process for different positions.

Good luck with your interview!