Barclays is a leading global financial institution with a rich history of over 329 years in innovation and customer service. They are a major player in the financial industry, offering diverse opportunities across various domains.

The Data Analyst position at Barclays involves working with large datasets to provide data-driven insights for business strategies. Candidates should have strong skills in SQL and SAS, and experience in data analysis, visualization, and cross-functional collaboration. The interview process is comprehensive, including behavioral, technical, and situational-based assessments, with initial evaluations often conducted online.

In this guide, Interview Query walks you through the Barclays Data Analyst interview process and offers tips for success. Let's dive in!

Submitting Your Application

The first step in becoming a Data Analyst at Barclays is to submit a comprehensive application that clearly demonstrates your technical skills and eagerness to join the firm. Whether you are approached by a Barclays recruiter or apply independently, thoroughly review the job description and customize your CV to meet the listed prerequisites.

Adjusting your CV may involve using specific keywords to match the hiring manager's filters and writing a targeted cover letter. Make sure to highlight relevant skills and professional experiences.

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a recruiter from the Barclays Talent Acquisition Team will contact you to verify essential details about your experiences and skill levels. This screening will likely include behavioral questions.

In some instances, the hiring manager for the Barclays data analyst position might also participate in the call to discuss the role and the company. This is a good opportunity for surface-level technical and behavioral questions.

Expect this call to last about 30 minutes.

Online Assessments

Candidates who pass the recruiter screening will be invited to complete several online assessments, which can include:

1. Cognitive Assessment
2. Personality Assessment
3. Barclays Mindset Assessment

These assessments will help Barclays evaluate your problem-solving abilities, psychological tendencies, and alignment with the company’s values.

Technical Virtual Interview

After passing the online assessments, candidates will move on to a technical virtual interview. This stage typically involves video conferencing and screen sharing, lasting about 1 hour. Questions may cover Barclays' data systems, ETL pipelines, and SQL queries. You should be prepared for questions on SQL and SAS, such as:

• How to merge two tables
• Write a SQL query to find the second-highest salary from an "Employees" table

Additional take-home assignments might involve product metrics, analytics, and data visualization. Proficiency in hypothesis testing, probability distributions, and machine learning basics may also be evaluated.

Depending on the role's seniority, case studies and other real-world scenario questions may be included.

Onsite Interview Rounds

Successful candidates from the virtual interview will be invited for an onsite interview. This phase usually consists of multiple rounds, each designed to assess different skill sets:

1. Behavioral Round
2. Technical Round
3. Skills Evaluation
4. Future Career Planning

During these rounds, your technical aptitude, including programming and data analysis skills, will be scrutinized. If given pre-interview assignments, be prepared to present your work.

Quick Tips For Barclays Data Analyst Interviews

• Practice SQL and SAS intensively: Barclays places significant emphasis on these skills. Use Interview Query to practice various SQL queries and SAS-related problems.
• Understand Barclays’ Values: Align your responses to show how you fit within the company's culture and values.
• Prepare for Situational Questions: Be ready for scenario-based questions, common in Barclays interviews, to assess your problem-solving and decision-making abilities.

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

• Create a function sorting to sort a list of strings in ascending alphabetical order from scratch.

• 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?

• What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

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

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• 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.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where bagging (e.g., random forest) is preferred for reducing variance and boosting (e.g., AdaBoost) is preferred for reducing bias. Provide examples of tradeoffs between the two.

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

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.

• List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques. Highlight how Lasso performs feature selection by shrinking coefficients to zero, while Ridge penalizes large coefficients without eliminating features.

• What are the key differences between classification models and regression models? Describe the fundamental differences between classification models, which predict categorical outcomes, and regression models, which predict continuous outcomes. Discuss their respective use cases and evaluation metrics.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current format. Suggest formatting changes and discuss common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? Given data on marketing channels and costs for a B2B analytics company, identify key metrics to determine the value of each marketing channel.

• 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.

• How would you investigate if an email campaign led to increased conversion rates? Analyze a scenario where a new email campaign coincides with an increase in conversion rates. Determine how to verify if the campaign caused the increase or if other factors were involved.

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

Q: What is the interview process like for a Data Analyst position at Barclays?
The interview process at Barclays for a Data Analyst position typically involves multiple stages. It starts with an online application and assessment, followed by a series of interviews: initial phone or video screening, technical interview, and behavioral interview. Expect questions on SQL, SAS, data analysis techniques, and Barclays’ values.

Q: What technical skills are required for a Data Analyst at Barclays?
To thrive in a Data Analyst role at Barclays, you should have strong proficiency in SQL and SAS. Experience with data extraction, preprocessing, and visualization tools like Tableau is also beneficial. Basic knowledge of machine learning and a good understanding of relational and non-relational databases will set you apart.

Q: How can I prepare for the Barclays Data Analyst interview?
Prepare for the interview by practicing SQL queries, especially focusing on complex data manipulation tasks like merging tables. Review your SAS skills and be prepared for situational and behavioral questions. Use Interview Query to practice various technical questions and case studies to get familiar with potential scenarios.

Q: What is the work environment like at Barclays for a Data Analyst?
Barclays offers a dynamic and supportive work environment, with strong emphasis on values such as Respect, Integrity, Service, Excellence, and Stewardship. They promote a flexible working culture that allows integration of professional and personal lives. The company also values diversity and inclusion, fostering a workplace where everyone feels confident and included.

Q: What are the common interview questions for a Data Analyst position at Barclays?
Common interview questions for this position include technical questions on SQL and SAS, behavioral questions about teamwork and problem-solving, and situational questions that assess your alignment with Barclays’ values. You might be asked to discuss your experience with data visualization, your approach to data analysis projects, and how you handle multiple tasks.

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. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Barclays' interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Barclays Data Analyst interview question and challenge.

You can 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!