KeyBank Data Analyst Interview Questions + Guide in 2025

KeyBank is a national banking association that provides a wide range of financial services, including consumer and commercial banking, investment services, and risk management solutions.

The Data Analyst role at KeyBank is integral to the Risk Management Information Reporting group, focusing on credit risk metrics and reporting. This position encompasses the analysis and improvement of credit risk data processes, requiring a strong foundation in data development and an understanding of banking and credit data landscape. Key responsibilities include managing complex data sets, conducting thorough analyses to drive business decisions, mentoring team members, and collaborating with business partners to provide actionable insights. A successful candidate will possess advanced technical skills in SAS, SQL, and data visualization tools, alongside strong communication abilities and a proactive approach to problem-solving. Empathy towards the banking sector and a collaborative spirit are essential traits for thriving in this role aligned with KeyBank's commitment to data-driven decision-making and excellence in risk management.

This guide will equip you with the necessary insights and preparation strategies to excel in your interview for the Data Analyst position at KeyBank, setting you apart as a strong candidate.

The interview process for a Data Analyst position at KeyBank is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes various types of interviews, each designed to evaluate different competencies relevant to the role.

1. Initial Phone Interview

The process typically begins with a brief phone interview conducted by a recruiter. This initial conversation lasts around 30 minutes and focuses on your background, experience, and motivation for applying to KeyBank. The recruiter will also provide insights into the company culture and the specifics of the Data Analyst role. This is an opportunity for you to express your interest and ask preliminary questions about the position.

2. Case Interview

Following the initial screening, candidates may be invited to participate in a case interview. This round usually involves a current employee from the team and may include one or two additional interviewers. The case interview is designed to assess your analytical thinking and problem-solving abilities through a practical scenario that tests your basic math skills and data interpretation. The interviewers will be looking for your approach to the problem, your reasoning, and how you communicate your thought process.

3. In-Person or Virtual Panel Interview

The next step often involves a panel interview, which can be conducted in person or virtually. This round typically includes multiple interviewers from different departments, such as risk management and data governance. During this session, you will be asked a mix of behavioral and technical questions. Expect to discuss your previous experiences, methodologies you have employed in data analysis, and how you have collaborated with cross-functional teams. This is also a chance to demonstrate your understanding of banking and credit data, as well as your ability to communicate complex concepts clearly.

4. Final Interview

The final stage of the interview process may involve a more senior-level interview, where you will meet with higher management or executives. This interview focuses on your long-term career goals, your fit within the company’s strategic vision, and your ability to lead and mentor others. You may also be asked to present a project or analysis you have worked on in the past, showcasing your technical skills and business acumen.

Throughout the interview process, candidates are encouraged to ask questions and engage with the interviewers to demonstrate their interest in the role and the company.

Next, let’s explore the specific interview questions that candidates have encountered during this process.

Here are some tips to help you excel in your interview.

Understand the Role and Its Impact

Before your interview, take the time to deeply understand the responsibilities of a Data Analyst at KeyBank. This role is not just about crunching numbers; it involves proactively solving business problems and leveraging complex data to drive decisions. Familiarize yourself with the specific data governance and stewardship tasks, as well as how they relate to credit risk metrics and reporting. This knowledge will allow you to articulate how your skills and experiences align with the role's expectations.

Prepare for Case Interviews

Given that some candidates have reported case interviews as part of the process, practice solving case studies that involve data analysis and business problem-solving. Focus on demonstrating your thought process clearly and logically. Be prepared to explain your reasoning and the steps you would take to analyze data and derive insights. This will showcase your analytical skills and your ability to communicate complex ideas effectively.

Emphasize Communication Skills

KeyBank values strong communication skills, especially in a role that requires collaboration with various business partners. Be ready to discuss how you have effectively communicated technical concepts to non-technical stakeholders in the past. Prepare examples that highlight your ability to articulate the implications of data findings and how you have influenced decisions through your insights.

Showcase Your Technical Proficiency

Make sure to highlight your proficiency in relevant tools and technologies, such as SQL, SAS, and Excel. Be prepared to discuss specific projects where you utilized these tools to solve problems or improve processes. If you have experience with data visualization tools like Tableau, mention how you have used them to present data in a compelling way that drives business decisions.

Be Personable and Engaging

Candidates have noted that the interviewers at KeyBank are pleasant and create a relaxed atmosphere. Approach the interview as a conversation rather than a formal interrogation. Engage with your interviewers by asking thoughtful questions about the team, the projects they are working on, and the company culture. This will not only help you gauge if KeyBank is the right fit for you but also demonstrate your genuine interest in the role and the organization.

Prepare for Behavioral Questions

Expect to answer behavioral questions that assess your problem-solving abilities, teamwork, and leadership potential. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare examples that illustrate how you have navigated challenges, collaborated with others, and contributed to team success in previous roles.

Align with Company Values

KeyBank emphasizes a culture of diversity, equity, and inclusion. Familiarize yourself with their core values and think about how your personal values align with those of the company. Be prepared to discuss how you can contribute to fostering an inclusive environment and how your unique background and experiences can add value to the team.

Follow Up Thoughtfully

After your interview, send a personalized thank-you email to your interviewers. Reference specific topics discussed during the interview to reinforce your interest in the role and the company. This not only shows your appreciation but also keeps you top of mind as they make their decision.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Analyst role at KeyBank. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at KeyBank. The interview process will likely focus on your analytical skills, understanding of data management, and ability to communicate insights effectively. Be prepared to discuss your experience with data analysis tools, methodologies, and your approach to problem-solving in a banking context.

Data Analysis and Tools

1. Can you describe your experience with SQL and how you have used it in your previous roles?

Understanding SQL is crucial for a Data Analyst role, as it is often used for querying databases and managing data.

How to Answer

Discuss specific projects where you utilized SQL, focusing on the complexity of the queries you wrote and the outcomes of your analyses.

Example

“In my previous role, I used SQL to extract and analyze customer transaction data, which involved writing complex joins and subqueries to identify trends in spending behavior. This analysis helped the marketing team tailor their campaigns, resulting in a 15% increase in customer engagement.”

2. What data visualization tools have you used, and how do you decide which one to use for a specific project?

Data visualization is key in presenting findings to stakeholders.

How to Answer

Mention specific tools you are familiar with, such as Tableau or Power BI, and explain your decision-making process for selecting a tool based on the project requirements.

Example

“I have extensive experience with Tableau and Power BI. I typically choose Tableau for its advanced visualization capabilities when presenting complex data, while I prefer Power BI for its integration with Microsoft products when working with internal teams.”

3. Describe a time when you had to clean and prepare a large dataset for analysis. What steps did you take?

Data preparation is a critical step in the analysis process.

How to Answer

Outline the specific steps you took to clean the data, including handling missing values, outliers, and ensuring data integrity.

Example

“In a recent project, I was tasked with cleaning a dataset of customer feedback. I identified and removed duplicates, filled in missing values using interpolation, and standardized the format of the data. This preparation allowed for more accurate sentiment analysis and actionable insights.”

4. How do you ensure the accuracy and integrity of your data analyses?

Accuracy is paramount in data analysis, especially in a banking environment.

How to Answer

Discuss the methods you use to validate your data and analyses, such as cross-referencing with other data sources or peer reviews.

Example

“I always cross-verify my findings with multiple data sources and conduct peer reviews to ensure accuracy. Additionally, I implement automated checks within my data pipelines to catch any anomalies early in the process.”

5. Can you explain a complex analytical concept to a non-technical audience?

Communication skills are essential for a Data Analyst, especially when presenting to stakeholders.

How to Answer

Provide an example of a time you successfully communicated a complex concept, focusing on how you simplified the information.

Example

“When presenting a predictive model to the marketing team, I used visual aids to illustrate how the model worked and its implications for their campaigns. I avoided technical jargon and focused on the business impact, which helped them understand the value of the analysis.”

Banking and Financial Knowledge

1. What do you understand about credit risk, and how would you analyze it using data?

Knowledge of credit risk is important for a Data Analyst in a banking context.

How to Answer

Discuss your understanding of credit risk metrics and how you would approach analyzing them using data.

Example

“I understand that credit risk involves the potential for loss due to a borrower's failure to repay a loan. I would analyze historical loan performance data, looking at default rates and borrower characteristics, to identify trends and develop predictive models for future risk assessment.”

2. How do you stay updated on industry trends and changes in banking regulations?

Staying informed is crucial in the ever-evolving banking sector.

How to Answer

Mention specific resources you use to keep up with industry news, such as financial news websites, regulatory bodies, or professional networks.

Example

“I regularly read industry publications like The Wall Street Journal and follow updates from the Federal Reserve. I also participate in webinars and professional groups focused on banking analytics to stay informed about regulatory changes and best practices.”

3. Describe a time when you identified a significant risk or opportunity through your analysis.

This question assesses your ability to provide actionable insights.

How to Answer

Share a specific example where your analysis led to a significant business decision.

Example

“While analyzing customer transaction data, I identified a segment of high-value customers who were not utilizing our premium services. I presented this finding to the product team, which led to targeted marketing efforts that increased service adoption by 20%.”

4. What methodologies do you use for data analysis in a financial context?

Understanding various methodologies is important for effective analysis.

How to Answer

Discuss specific methodologies you are familiar with, such as regression analysis, time series analysis, or machine learning techniques.

Example

“I often use regression analysis to understand relationships between variables, such as how interest rates affect loan demand. For time-sensitive data, I apply time series analysis to forecast trends and make informed recommendations.”

5. How would you approach a project where you need to analyze customer behavior data?

This question evaluates your analytical approach and problem-solving skills.

How to Answer

Outline your step-by-step approach to analyzing customer behavior data, from data collection to presenting findings.

Example

“I would start by defining the objectives of the analysis, then gather relevant data from various sources, such as transaction logs and customer surveys. After cleaning and preparing the data, I would use segmentation techniques to identify patterns in behavior, ultimately presenting actionable insights to the marketing team.”