Barclays Data Analyst Interview Questions + Guide in 2024
Overview
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 SQL and SAS skills and experience in data analysis, visualization, and cross-functional collaboration. The interview process includes behavioral, technical, and situational-based assessments, with initial evaluations often conducted online.
In this guide, Interview Query walks you through the interview process and commonly asked Barclays data analyst interview questions, ultimately offering tips for success. Let’s dive in!
Cultural and Behavioral Questions
This feature is currently experimental, and we’re committed to improving it with your valuable feedback.Can you describe a situation where you encountered a particularly troublesome data set during a project? What steps did you take to clean, analyze, and derive meaningful insights from that data? How did your actions impact the overall project outcome?
In a previous project, I worked with a data set that had numerous inconsistencies and missing values, which posed a significant challenge. To address this, I first conducted an exploratory data analysis (EDA) to identify the extent of the issues. I then implemented a systematic approach to data cleaning, which included removing duplicates, filling in missing values using statistical methods, and transforming data formats to ensure consistency. Once the data was clean, I utilized various analytical techniques to extract insights, which ultimately led to a more informed decision-making process. This experience taught me the importance of thorough data preparation and its direct influence on the quality of analysis.
Share an experience where you had to collaborate with a team to achieve a common goal in a data analysis project. What was your role, and how did you ensure effective communication and collaboration?
In one project, our team was tasked with analyzing customer behavior to enhance our product offerings. I took on the role of data analyst, collaborating closely with marketing and sales teams. To ensure effective communication, we held regular meetings to discuss progress and challenges. I established a shared document where team members could contribute insights and updates. This collaborative approach not only fostered a sense of ownership among team members but also led to a comprehensive analysis that improved our product strategy, illustrating the value of teamwork in achieving shared objectives.
Can you provide an example of a time when a significant change occurred during a project you were working on? How did you adapt to this change, and what was the result?
During a project aimed at analyzing market trends, we suddenly received new data that necessitated a shift in our analytical approach. Initially, this was quite challenging as it required re-evaluating our previous assumptions. I quickly organized a meeting with the team to discuss the implications of this new data. Together, we revised our analysis plan and reassigned tasks based on individual strengths. By adapting swiftly and maintaining open communication, we managed to deliver a revised analysis that incorporated the new insights, thereby enhancing the project’s value.
Barclays Data Analyst Interview Process
The interview process usually depends on the role and seniority; however, you can expect the following on a Barclays data analyst interview:
Recruiter/Hiring Manager Call Screening
If your CV is shortlisted, a Barclays Talent Acquisition Team recruiter 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:
- Cognitive Assessment
- Personality Assessment
- 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.
Case studies and other real-world scenario questions may be included depending on the role’s seniority.
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:
- Behavioral Round
- Technical Round
- Skills Evaluation
- Future Career Planning
These rounds will scrutinize your technical aptitude, including programming and data analysis skills. If given pre-interview assignments, be prepared to present your work.
What Questions Are Asked in a Barclays Data Analyst Interview?
Typically, interviews at Barclays vary by role and team, but commonly, Data Analyst 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 from scratch.
2. How would you design a function to detect anomalies in univariate and bivariate datasets?
How would you design a function to detect anomalies if given a univariate dataset? What if the data is bivariate?
3. 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.
4. 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?
5. How would you explain a p-value to a non-technical person?
How would you explain a p-value to someone who is not technical?
6. 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?
7. How does random forest generate the forest and why use it over logistic regression?
Explain how a 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. 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.
9. 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 new model’s success, such as accuracy, precision, recall, and AUC-ROC.
10. 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.
11. What are the key differences between classification 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.
12. 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.
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 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.
How to Prepare for a Data Analyst Interview at Barclays
To help you succeed in your Barclays data analyst interviews, consider these tips based on interview experiences:
Practice SQL and SAS intensively: Barclays emphasizes these skills significantly. 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.
FAQs
What is the average salary for a Data Analyst at Barclays?
Average Base Salary
Average Total Compensation
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.
What is the work environment like for a Data Analyst at Barclays?
Barclays offers a dynamic and supportive work environment with a strong emphasis on Respect, Integrity, Service, Excellence, and Stewardship. They promote a flexible working culture that integrates professional and personal lives. The company also values diversity and inclusion, fostering a workplace where everyone feels confident and included.
Conclusion
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!