General Motors Data Analyst Interview Questions + Guide in 2025

General Motors (GM) is a global automotive industry leader focused on innovation and sustainability, committed to creating a world with zero crashes, zero emissions, and zero congestion.

As a Data Analyst at GM, you will play a pivotal role in providing analytical support and reporting for the Commercial Lending organization. Your key responsibilities will include designing and delivering analytic reports and data visualizations that extract meaningful insights for senior leadership. You will work closely with cross-functional teams to enhance and automate reporting processes utilizing tools like SAS and PowerBI, ensuring the accuracy and integrity of data through rigorous quality checks. Additionally, you will assist in developing advanced predictive models and contribute to corporate projects, making productive contributions that align with GM's strategic priorities. A strong understanding of SQL, SAS, and data visualization techniques is essential, along with excellent communication skills to tailor your insights based on the audience.

To excel in this role, you should possess a bachelor's degree in a relevant field such as Mathematics, Statistics, or Business Administration, along with 2-3 years of experience in an analytical role. You will thrive at GM if you are passionate about data-driven decision-making and have a keen ability to cultivate relationships and earn trust within your team and with stakeholders.

This guide will help you prepare for your interview by providing insights into the expectations and skills needed for the Data Analyst role at GM, enabling you to showcase your qualifications effectively.

The interview process for a Data Analyst position at General Motors is structured and involves several key stages designed to assess both technical and behavioral competencies.

1. Application and Initial Screening

The process begins with submitting an online application, which is followed by an initial screening call with a recruiter. This call typically lasts around 15-30 minutes and focuses on your background, interest in the role, and basic qualifications. The recruiter will also provide insights into the company culture and the specifics of the Data Analyst position.

2. HireVue Video Interview

Candidates who pass the initial screening are invited to complete a HireVue video interview. This stage consists of a series of pre-recorded questions that you will answer on camera. You will have a limited time to prepare for each question, usually around 30 seconds, followed by a few minutes to record your response. The questions often focus on behavioral scenarios, such as teamwork, problem-solving, and overcoming challenges. Additionally, some candidates may encounter cognitive games designed to assess analytical thinking and problem-solving skills.

3. Technical Assessment

Following the HireVue interview, candidates may be required to complete a technical assessment. This typically involves coding challenges that test your proficiency in SQL and data manipulation. The assessment may include tasks such as writing SQL queries, performing data analysis, and troubleshooting code issues. Candidates are encouraged to practice coding problems on platforms like LeetCode or HackerRank to prepare for this stage.

4. In-Person or Virtual Interviews

Successful candidates from the technical assessment will be invited to participate in one or more in-person or virtual interviews with hiring managers and team members. These interviews are generally structured as one-on-one sessions and may include both behavioral and technical questions. Interviewers will assess your analytical skills, understanding of data visualization tools (like Power BI), and your ability to communicate complex data insights effectively. Expect to discuss your previous experiences and how they relate to the responsibilities of the Data Analyst role.

5. Final Interview and Offer

The final stage may involve a wrap-up interview with senior leadership or a panel of interviewers. This is an opportunity for you to ask questions about the team, projects, and company culture. If all goes well, you will receive a job offer, which may include discussions about salary, benefits, and work arrangements.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during the process.

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

Prepare for the HireVue Assessment

The first step in the interview process often involves a HireVue coding assessment. Familiarize yourself with the platform and practice coding problems on sites like LeetCode or HackerRank. Focus on common algorithms and data structures, as well as specific tasks like calculating values or identifying "happy numbers." Make sure to read the problem statements carefully to avoid misunderstandings, as clarity is key to successfully completing the assessment.

Master Behavioral Questions

Expect a significant focus on behavioral questions during your interviews. GM values candidates who can demonstrate their problem-solving abilities and teamwork skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare examples that highlight your experiences overcoming challenges, learning new skills quickly, and collaborating effectively with others. Tailor your stories to reflect the values and culture of GM, emphasizing integrity, innovation, and community.

Showcase Your Technical Skills

While behavioral questions are prevalent, don't neglect the technical aspects of the role. Be prepared to discuss your experience with SQL, SAS, and data visualization tools like Power BI. Brush up on your knowledge of data analytics concepts and be ready to explain how you've applied these skills in previous roles. If you have experience with predictive modeling or data quality checks, be sure to highlight that as well.

Understand the Company Culture

General Motors promotes an inclusive culture that values innovation and teamwork. Familiarize yourself with GM's core values and mission, particularly their commitment to community and customer service. During the interview, express your alignment with these values and how you can contribute to fostering a positive work environment. Show enthusiasm for being part of a team that drives strategic decisions in the automotive finance sector.

Be Ready for a Hybrid Work Environment

As the role follows a hybrid work model, be prepared to discuss your experience with remote collaboration tools and how you manage your time effectively in a flexible work environment. Highlight any previous experience working in hybrid or remote settings, and share strategies you use to stay productive and engaged with your team.

Follow Up Thoughtfully

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Use this as a chance to reiterate your interest in the position and briefly mention a key point from your conversation that resonated with you. This not only shows your professionalism but also reinforces your enthusiasm for the role.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Data Analyst role at General Motors. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at General Motors. The interview process will likely focus on a combination of technical skills, analytical thinking, and behavioral competencies. Candidates should be prepared to demonstrate their proficiency in data analysis tools, problem-solving abilities, and experience in collaborative environments.

Technical Skills

1. What is your experience with SQL, and can you explain how you would optimize a query?

Understanding SQL is crucial for a Data Analyst role, and interviewers will want to assess your technical skills in this area.

How to Answer

Discuss your experience with SQL, including specific examples of queries you've written. Explain the techniques you use to optimize queries, such as indexing or rewriting joins.

Example

"I have extensive experience with SQL, having used it to extract and manipulate data for various projects. To optimize a query, I typically analyze the execution plan to identify bottlenecks, and I often implement indexing on frequently queried columns to improve performance."

2. Can you describe a time when you had to clean and prepare a dataset for analysis?

Data cleaning is a critical part of the data analysis process, and interviewers will want to know how you handle this task.

How to Answer

Provide a specific example of a dataset you worked with, the challenges you faced, and the steps you took to clean and prepare the data.

Example

"In a previous role, I worked with a dataset that had numerous missing values and inconsistencies. I used Python's Pandas library to identify and fill missing values, and I standardized the formatting of date fields to ensure consistency across the dataset."

3. What data visualization tools are you familiar with, and how have you used them in your previous work?

Data visualization is essential for communicating insights, so be prepared to discuss your experience with relevant tools.

How to Answer

Mention specific tools you have used, such as Power BI or Tableau, and describe how you utilized them to present data effectively.

Example

"I am proficient in Power BI, which I used to create interactive dashboards for senior management. One project involved visualizing sales data trends, which helped the team identify key areas for improvement and ultimately led to a 15% increase in sales."

4. Explain the difference between inner join and outer join in SQL.

Understanding joins is fundamental for data manipulation, and interviewers may test your knowledge in this area.

How to Answer

Clearly define both types of joins and provide an example of when you would use each.

Example

"An inner join returns only the rows that have matching values in both tables, while an outer join returns all rows from one table and the matched rows from the other. I would use an inner join when I need to analyze data that is common to both datasets, whereas an outer join is useful when I want to include all records from one table, regardless of whether there is a match."

Behavioral Questions

5. Describe a situation where you had to work with a difficult team member. How did you handle it?

Team dynamics are important, and interviewers want to see how you navigate challenges in collaboration.

How to Answer

Share a specific example, focusing on your approach to resolving the conflict and the outcome.

Example

"I once worked on a project with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our differing perspectives and actively listened to their concerns. By finding common ground, we were able to collaborate more effectively, and the project was completed successfully."

6. Tell me about a time you had to learn a new tool or technology quickly. How did you approach it?

Adaptability is key in a fast-paced environment, and interviewers will want to know how you handle learning curves.

How to Answer

Discuss the specific tool or technology, your learning process, and how you applied it in your work.

Example

"When my team decided to implement Power BI for our reporting needs, I took the initiative to learn it quickly. I enrolled in an online course and practiced by creating sample dashboards. Within a few weeks, I was able to lead a training session for my colleagues, which helped us transition smoothly to the new tool."

7. Can you provide an example of a project where your analysis led to a significant business decision?

This question assesses your impact as a Data Analyst and your ability to drive results.

How to Answer

Describe the project, your analysis, and the decision that was made as a result of your work.

Example

"I conducted an analysis of customer feedback data that revealed a significant drop in satisfaction related to our product delivery times. I presented my findings to management, which led to a strategic decision to revamp our logistics process. As a result, we improved delivery times by 20% and saw a corresponding increase in customer satisfaction scores."

8. Where do you see yourself in five years?

This question helps interviewers understand your career aspirations and alignment with the company's goals.

How to Answer

Discuss your professional goals and how they relate to the role and company.

Example

"In five years, I see myself in a senior data analyst role, leading projects that drive strategic decisions. I am particularly interested in developing my skills in predictive analytics and contributing to innovative solutions that enhance customer experiences at GM."

Industry Knowledge

9. What trends do you see impacting the automotive finance industry?

Understanding industry trends is crucial for a Data Analyst role, especially in a specialized field like automotive finance.

How to Answer

Discuss current trends and their potential implications for the industry.

Example

"I see a growing trend towards digital transformation in the automotive finance industry, with more companies adopting data analytics and AI to enhance customer experiences. This shift will likely lead to more personalized financing options and improved risk assessment models."

10. How would you approach developing a predictive model for loan default risk?

This question assesses your understanding of predictive modeling and its application in the finance sector.

How to Answer

Outline the steps you would take to develop the model, including data collection, feature selection, and validation.

Example

"I would start by gathering historical loan data, including borrower demographics and payment history. Next, I would perform exploratory data analysis to identify key features that correlate with default risk. After selecting the relevant features, I would use machine learning algorithms to build the model and validate its performance using cross-validation techniques."