Gartner Data Analyst Interview Questions + Guide in 2024

Gartner is a global research and advisory firm that provides insights, advice, and tools for leaders in various industries to achieve their mission-critical priorities. Renowned for its expertise, Gartner supports over 15,000 client enterprises in nearly 100 countries with keen analysis and bold ideas.

This guide will walk you through the interview process, covering typical Gartner data analyst interview questions and essential preparation tips. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a Gartner data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Gartner Talent Acquisition Team will make contact and verify critical details like your experiences and skill level. Behavioral questions may also be a part of the screening process, such as:

• Can you successfully use tools like pivot tables to do analysis?
• Are you good at working in remote team settings?
• Are you okay working for this role in the East Coast time zone?
• Have you collaborated with C-Suite senior managers before?

The recruiter may also ask about your experience with data queries using advanced Excel and other tools. Gartner hiring managers might also be present during the screening round to answer your queries about the role and the company, possibly indulging in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Gartner data analyst role is usually conducted through virtual means, including video conference and screen sharing. Questions in this one-hour interview stage may revolve around Gartner’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. In addition, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Case studies and similar real-scenario problems may also be assigned depending on the position’s seniority.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you may be invited to attend onsite interview rounds, where multiple interviews will be conducted with different teams and individuals within Gartner. Feedback that describes a session that involved an Excel test and discussions with various department managers emphasizes this.

Your technical skills, including programming and data analysis capabilities, will be evaluated thoroughly. You may also be required to make presentations based on take-home assignments or case study results.

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

1. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

2. Write a function to find the maximum number in a list of integers.

Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

3. Create a function convert_to_bst to convert a sorted list into a balanced binary tree.

Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should have a height difference of at most one between the left and right subtrees of all nodes.

4. Write a function to simulate drawing balls from a jar.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

5. Develop a function can_shift to check if one string can be shifted to become another.

Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

6. How would you explain linear regression to a child, a college student, and a mathematician?

Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience’s understanding level.

7. How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment?

As a data scientist at a bank, you need to build a decision tree model to predict if a borrower will repay a personal loan. Evaluate whether a decision tree is the correct model and how you would assess its performance before and after deployment.

8. How would you justify using a neural network model and explain its predictions to non-technical stakeholders?

Your manager asks you to build a neural network model to solve a business problem. Justify the complexity of the model and explain its predictions to non-technical stakeholders.

9. How does random forest generate the forest, and why use it over logistic regression?

Explain how random forest generates its forest of trees. Additionally, discuss why one might choose random forest over other algorithms like logistic regression.

10. What are the key differences between classification models and regression models?

Describe the main differences between classification models and regression models.

11. How much should a ride-sharing app budget for a $5 coupon initiative?

A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider and services (N) riders. Calculate the total budget needed for the coupon initiative.

12. What is a confidence interval for a statistic and why is it useful?

Explain what a confidence interval is, why it is useful to know the confidence interval for a statistic, and how to calculate it.

13. What is the probability of finding an item on Amazon’s website?

Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that a specific item X is available at warehouse A (0.6) or warehouse B (0.8), calculate the probability that the item X would be found on Amazon’s website.

14. Is a coin fair if it comes up tails 8 times out of 10 flips?

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

15. What are time series models and why are they needed?

Describe what time series models are and explain why they are necessary when less complicated regression models are available.

16. How would you forecast Facebook’s revenue for the next year?

You work on the revenue forecasting team at a company like Facebook. An executive asks you to forecast the company’s revenue for the coming year. How would you approach this task?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Gartner data analyst interview include:

• Brush Up on Technical Skills: Ensure that your Excel skills, including VBA and pivot tables, are sharp. Practice SQL, Python, or other relevant data-query languages that might be required for the position.
• Understand Gartner’s Industry: Be well-versed with Gartner’s research methods and industry position. Having knowledge about the company’s data management processes and systems is also crucial.
• Prepare for Case Studies: Be ready to solve real-world data problems and communicate your findings clearly. Practicing with similar business problems beforehand can be very beneficial.

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

According to Glassdoor, Data Analysts at Gartner earn between $80K to $112K per year, with an average of $95K per year.

What skills are essential for a Data Analyst at Gartner?

Essential skills include proficiency in advanced Excel, SQL, and experience with data visualization tools like Tableau or Power BI. Strong problem-solving abilities, effective communication, and experience with statistical analysis and programming languages such as Python or R are also important.

What is Gartner’s work environment and culture like?

Gartner fosters a collaborative and inclusive work environment with a focus on diversity. The company offers a hybrid work environment that includes both remote work and in-office collaboration. Gartner values creativity, continuous improvement, and professional growth.

What are the main responsibilities of a Data Analyst at Gartner?

Responsibilities include performing detailed data analysis to provide actionable insights, building dashboards and reports, collaborating with stakeholders to understand business needs, and using data to solve complex problems. Analysts also engage in process improvements and ensure data quality.

Are you ready to elevate your career and take on new challenges in data analysis? Gartner offers a dynamic and inclusive work environment where your skills can flourish and make a substantial impact. Immerse yourself in impactful projects, collaborate with industry leaders, and grow professionally in a company committed to excellence and diversity.

For more insights into Gartner’s hiring process, explore our comprehensive Gartner Interview Guide on Interview Query. We cover key interview questions, detailed processes for various roles, and tips to help you succeed. Give yourself the edge you need to conquer your next career milestone with confidence.

Good luck with your interview!