McKinsey Data Analyst Interview Questions + Guide in 2024

McKinsey & Company is a multinational consulting firm that serves businesses, governments and nonprofits. They provide a wide range of consulting services, including in data analytics.

Data analysts at McKinsey are embedded across the organization, and as consultants, they serve clients in a variety of capacities. On the analytics team, McKinsey data analysts are responsible for building and establishing clients’ analytics capabilities, helping them to better leverage their data. McKinsey also hires data analysts to serve clients in marketing, operations and organizational development.

To become a data analyst at McKinsey, collaboration and communication are essential skills. In consulting roles, data analysts must be skilled at translating client questions into analytics problems, and ultimately conveying data insights to clients. This guide offers a look at roles and responsibilities, frequently asked McKinsey data analyst interview questions as well as the data analyst interview process.

McKinsey & Company has 12 teams focused on different business functions, from analytics to marketing and sales. Data analysts work on nearly every team, helping clients to generate insights from data. At McKinsey & Company, data analysts work on these teams primarily:

Analytics

McKinsey specializes in helping organizations to better leverage data. Many data analysts work at McKinsey work on analytics teams, and perform a variety of tasks from designing analytics solutions for clients to performing analysis.

Technology & Digital

Data analysts on the Technology & Digital team help clients build next-generation analytics solutions, as well as perform analysis of a range of data. This team is tasked with helping businesses leverage cutting-edge technology and fully integrate it into their operations.

Marketing & Sales

The Marketing & Sales team empowers organizations to leverage marketing data for growth. Data analysts on Marketing & Sales work on a variety of projects, from ETL to building dashboards and marketing analytics solutions for clients.

People & Operational Performance

Analysts on the People & Operational Performance team are tasked with generating operations insights for businesses, and helping to improve efficiency, retain talent, and enhance operational performance.

Operations

McKinsey’s Operations team connects strategy, technology, and digital transformation to help organizations achieve sustainable growth. Data analysts on Operations help businesses make sense of operations data to make the operation more efficient.

As a consulting firm, McKinsey & Company requires data analysts to be strong communicators and collaborators. This is a necessary job skill across all departments. In particular, McKinsey data analysts are responsible for translating clients’ questions into analytical problems.

Although responsibilities change by role and engagement, a data analyst may be responsible for managing the entire data cleaning pipeline, using data visualization, and ultimately generating insights from an organization’s data and communicating that back to clients. Key job responsibilities include:

• Responsible for performing data ingestion, data transformation and data loading ingot applications
• Data validation and data cleaning
• Bachelor’s degree in computer science, information technology, engineering, or related field (some roles require advanced degrees)
• 2-4 years of experience in IT, ETL, and/or data analysis
• Expertise in SQL, Python and Excel
• Experience with Hadoop, Spark, AWS, MS Azure
• Excellent time management skills
• Strong interpersonal skills and a client-oriented mindset
• Strong problem-solving skills and an action-oriented mindset

McKinsey & Company has one of the most rigorous interview processes in the industry. Although there are some similarities between McKinsey analyst interviews and others in the tech industry, there are some distinct differences including:

• Problem Solving Game - A key tenant of the McKinsey recruiting process, the problem-solving game, called Solve, is the first step to landing an interview. This is a gamified test with logic-based and probability questions. See a guide to Solve.

• Technical Expertise Interview (TEI) - The TEI usually comes after initial interviews, and is designed to assess your technical skills. This may include a QuantHub assessment. You should expert intermediate SQL questions, as well as questions related to data processing, data modeling, and data visualization. Examples of questions include:

  • Find standard deviation from a set of numbers.
  • What is mean? What is the standard deviation?
  • What are the assumptions of linear regression?

• Case Interviews - McKinsey case interviews test your ability to approach problems, solve them creatively, your business sense, and your communication skills. Review the company’s sample case studies, which provide example solutions and frameworks. With case study interviews, you should be prepared to:

  • Identify areas you would look to solve the problem
  • Solve some quantitative questions
  • Outline initial conclusions
  • Make an overall recommendation

• Personal Experience Interview (PEI) - The PEI is a type of behavioral and culture fit interview, which assesses your work experiences and provides insights into what motivates you. In particular, you should convey the impact you’ve had in your work, your entrepreneurial drive, your leadership style, and problem-solving approach in the PEI. Sample questions include:

  • Explain a challenging situation you encountered when working with someone with an opposing opinion.
  • Talk about a time you worked to achieve something that was outside your comfort zone.
  • Discuss a hypothetical client scenario to help us understand how you structure tough, ambiguous challenges, identify important issues, deal with the implications of facts and data, formulate conclusions and recommendations, and articulate your thoughts.
  • Share an example of a time you effectively worked with people with different backgrounds.

For more information, check out McKinsey’s interview resources page, which features videos about each interview segment, as well as practice guides and more.

Here are some example McKinsey data analyst interview questions:

1. What is a stored procedure?

Stored procedures is SQL code that a user creates that can be reused over and over again. Stored procedures are used primarily for quickly executing commonly used SQL queries.

2. What is the difference between truncate and delete?

The DELETE command is used to delete specified rows(one or more), while TRUNCATE is used to delete all the rows from a table. TRUNCATE is faster, but you cannot roll back the data after using TRUNCATE.

3. Tell me about a time you had a conflict with a colleague.

This is a commonly asked data analyst behavioral question in McKinsey interviews.

4. What’s the significance of normalizing data?

Normalizing data is the process of organizing a database to improve data integrity. Data normalization helps to reduce duplicate data, leads to faster analysis, and streamlines data segmentation.

5. How would you explain what a p-value is to someone who is not technical?

A p-value is a statistical measure that helps determine the significance of results in a hypothesis test. P-values are used primarily to assess whether the observed data could have occurred under the null hypothesis.

6. Estimate the cost of storing Google Earth photos each year

Estimating the cost of storing Google Earth photos annually involves understanding the scale and requirements of satellite imagery. Specifically, this calculation focuses on the number of photos needed to cover the Earth’s surface and the associated storage costs.

More McKinsey Data Analyst Interview Questions

See the salary range for data analysts at McKinsey: