Gartner is a global research and advisory company that provides insights, advice, and tools for leaders in IT, finance, HR, customer service, and support, and other areas. Founded in 1979, Gartner has grown significantly and now serves over 15,000 client enterprises in more than 100 countries with a team of over 19,500 associates worldwide.

As a mid-level Data Engineer at Gartner, you will be at the forefront of developing scalable and configurable data engineering pipelines. Your role will include designing and implementing high-performance data solutions, automating data pipelines, and integrating a wide variety of data sources. Expertise in Python, AWS/GCP, Azure, and SQL will be critical for success. Join a dynamic team that values innovation, professional growth, and collaboration.

Ready to embark on a rewarding career with Gartner? This guide will navigate you through the interview process and help you prepare for your role as a Data Engineer.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Gartner as a Data Engineer. Whether you were contacted by a Gartner recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

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 key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Gartner data engineer hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge 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 present you with an invitation for the technical screening round. Technical screening for the Gartner data engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Gartner’s data systems, ETL pipelines, SQL queries, and big data stack.

You may receive take-home assignments related to coding, building data pipelines, or data transformation. Your proficiency in Python, Azure, statistics, and probability distributions might also be assessed.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Gartner office. Your technical prowess, including programming and data engineering capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data engineer role at Gartner.

Quick Tips For Gartner Data Engineer Interviews

• Plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. Some tips for acing your Gartner interview include:

• Familiarize with Azure Tools: Be ready to answer detailed questions about ADF, ADLS, Synapse Analytics, Azure Functions, and overall Azure architecture. Experience with building data pipelines and handling data integration using these tools can give you an edge.

• Know the Big Data Stack: Questions related to Big Data technologies like Spark, Hive, and HDFS are common. Make sure you can discuss your experience or understanding of these technologies convincingly.

• Emphasize Problem-Solving Skills: Gartner’s interviews often assess your problem-solving abilities. Practice solving complex data engineering scenarios and emphasize your capability to tackle real-world data problems.

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

• 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.

• 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.

• 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 be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• 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.

• Develop a function can_shift to determine 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.

• 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.

• What happens when you run logistic regression on perfectly linearly separable data? Given a dataset of perfectly linearly separable data, describe the outcome when logistic regression is applied.

• How would you evaluate and deploy a decision tree model for loan repayment prediction? As a data scientist at a bank, you need to build a decision tree model to predict loan repayment. Evaluate if a decision tree is the correct model and describe how you would assess its performance before and after deployment.

• How would you justify using a neural network to non-technical stakeholders? If asked to build a neural network model for a business problem, explain how you would justify its complexity and explain the predictions to non-technical stakeholders.

• How does random forest generate the forest and why use it over logistic regression? Describe how random forest generates its ensemble of trees and explain why it might be preferred over logistic regression for certain problems.

• How much should we budget for the coupon initiative in total? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider. The app services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one rider will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

• 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, and how to calculate it.

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

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

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary when simpler regression models exist.

• - How would you forecast Facebook's revenue for the next year? You work on the revenue forecasting team at Facebook. An executive asks you to predict the company's revenue for the coming year. How would you approach this task?

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

Q: What is the interview process like for a Data Engineer position at Gartner?
The interview process typically includes several rounds. Initial steps involve a phone screen with a recruiter, followed by technical interviews which may focus on coding, statistics, and probability. Behavioral interviews and cultural fit assessments are also standard. Some candidates experience coding tests, while others may encounter project-based discussions and technical assessments from managers or VPs.

Q: What are the typical questions asked during the interviews?
Interview questions often cover a wide range of topics including Python and SQL proficiency, machine learning concepts, big data stack knowledge, Azure data services like Azure Data Factory and Azure SQL, and practical coding challenges. You might also be asked about pipeline building, coding past projects, and problem-solving within Azure environments.

Q: How can I prepare for an interview at Gartner?
To prepare, review your knowledge in Python, SQL, and big data stack technologies. Familiarize yourself with Azure services like Data Factory, Databricks, and Synapse. Practicing coding challenges and conceptual questions about data engineering on platforms like Interview Query can also be beneficial. Don’t forget to review your past projects and be ready to discuss them in detail.

Q: What’s the company culture like at Gartner?
Gartner boasts a culture of nonstop innovation, collaboration, and inclusivity. They value diverse experiences and encourage creativity and the pursuit of new ideas. The company provides a supportive environment with opportunities for growth and development, making it a great place to work and learn.

Q: What can I expect regarding benefits and compensation?
Gartner offers competitive compensation packages, including performance-based bonuses. Benefits include medical, dental, and vision plans, a 401K with corporate match, extensive paid time off, parental leave, tuition reimbursement, and more. They emphasize the well-being of their employees and provide numerous resources for personal and professional growth.

The interview process at Gartner for the Data Engineer position offers a mixed experience. Some applicants have reported a streamlined process with prompt feedback and professional interactions, while others have experienced a lack of follow-up after the interview stages. This inconsistency suggests varying degrees of candidate experience based on the team and location.

If you seek specific insights and guidance on navigating Gartner’s interview process, our main Gartner Interview Guide is an invaluable resource. Here, you can explore potential interview questions and detailed analyses of the interview stages. Additionally, we offer guides for other roles, providing a broader understanding of Gartner’s comprehensive interview process.

At Interview Query, we are dedicated to empowering you with the knowledge, confidence, and strategic insights to excel in every aspect of your Gartner Data Engineer interview. Dive into our guides and ace your next interview!

Good luck with your future endeavors!