GroupM Data Engineer Interview Questions + Guide in 2025

GroupM is WPP’s media investment group and the world’s leading media investment company, dedicated to shaping the next era of media where advertising operates more effectively for consumers.

The Data Engineer at GroupM plays a pivotal role in designing and building cloud-based data solutions that facilitate business intelligence and data quality compliance tools. This position involves collaborating closely with various internal teams, including Data Strategy, Analytics, Investment, and Planning, to ensure alignment with organizational goals. Key responsibilities include developing and maintaining data pipelines, migrating legacy data systems to modern platforms, and automating quality assurance processes. A successful candidate will possess advanced knowledge of databases and programming languages such as SQL and Python, along with familiarity with cloud platforms like AWS and GCP. Individuals who thrive in this role are detail-oriented, possess strong organizational skills, and excel in fostering collaborative relationships across cross-functional teams.

This guide will provide you with valuable insights and tailored strategies to help you prepare for your interview at GroupM, enhancing your confidence and increasing your chances of success.

The interview process for a Data Engineer position at GroupM is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes initial screenings and several rounds of interviews, focusing on various competencies relevant to the role.

1. Initial Phone Screening

The process begins with a phone interview conducted by a recruiter. This initial screening typically lasts around 30 minutes and serves as an opportunity for the recruiter to gauge your background, skills, and interest in the position. Candidates should be prepared to discuss their experience with data engineering, relevant technologies, and their understanding of GroupM's operations. It’s also a chance for candidates to ask questions about the company culture and the specifics of the role.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via video call. This assessment focuses on core data engineering skills, including proficiency in SQL and programming languages such as Python. Candidates might be presented with coding challenges or problem-solving scenarios that reflect real-world tasks they would encounter in the role. Familiarity with data manipulation, cloud platforms, and data architecture will be crucial during this stage.

3. Onsite Interviews

The onsite interview typically consists of multiple rounds, often four, each lasting approximately 45 minutes. These interviews are conducted by various team members, including directors and senior engineers, who will evaluate both technical and behavioral competencies. Candidates can expect questions that explore their past experiences, conflict resolution skills, and their ability to work collaboratively within a team. Additionally, there may be case studies or practical exercises that require candidates to demonstrate their analytical skills and approach to data-related challenges.

4. Final Interview

The final interview may involve a more in-depth discussion with higher-level management or team leads. This stage often focuses on assessing the candidate's alignment with GroupM's values and their potential contributions to the team. Candidates should be prepared to discuss their long-term career goals and how they envision their role within the company.

Throughout the interview process, candidates are encouraged to showcase their technical expertise, problem-solving abilities, and interpersonal skills, as these are critical for success in the Data Engineer role at GroupM.

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

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

Take Initiative in Communication

Given the feedback from previous candidates, it’s clear that proactive communication is key. Don’t hesitate to reach out to the recruiter if you haven’t heard back after your initial application or interview. This shows your enthusiasm for the role and helps you stay informed about your application status. Prepare thoughtful questions to ask during your conversations, demonstrating your interest in the company and the position.

Prepare for Technical Assessments

Expect a strong focus on SQL and coding skills during the interview process. Brush up on your SQL knowledge, particularly complex queries, joins, and subqueries. Familiarize yourself with coding challenges similar to those found on platforms like Leetcode. Additionally, be prepared to discuss your experience with data engineering tools and cloud platforms, as these are crucial for the role.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your problem-solving abilities. Be ready to discuss specific instances where you managed conflicts or overcame challenges in your previous roles. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate your thought process and the impact of your actions.

Emphasize Collaboration and Communication

GroupM values collaboration across teams, so be prepared to discuss how you’ve worked with cross-functional teams in the past. Highlight your ability to communicate effectively with both technical and non-technical stakeholders. Share examples of how you’ve facilitated discussions, gathered requirements, or resolved conflicts to achieve project goals.

Understand the Company Culture

GroupM is committed to employee growth and fostering a collaborative environment. Familiarize yourself with their values and culture, and be prepared to discuss how your personal values align with theirs. Show enthusiasm for the opportunity to contribute to a team that prioritizes innovation and responsible media investment.

Be Ready for Behavioral Questions

Expect behavioral questions that delve into your past experiences. Prepare to discuss your approach to project management, how you handle tight deadlines, and your strategies for ensuring data quality. Reflect on your previous roles and think of specific examples that demonstrate your skills and adaptability.

Stay Informed on Industry Trends

GroupM operates in a rapidly evolving media landscape. Stay updated on industry trends, data privacy laws, and emerging technologies relevant to data engineering. This knowledge will not only help you answer questions more effectively but also position you as a candidate who is genuinely interested in the field and the company’s mission.

Follow Up After the Interview

After your interview, send a thank-you email to express your appreciation for the opportunity to interview. This is also a chance to reiterate your interest in the role and briefly mention any key points you may not have had the chance to discuss during the interview. A thoughtful follow-up can leave a positive impression and keep you top of mind for the hiring team.

By following these tips and preparing thoroughly, you’ll be well-equipped to make a strong impression during your interview at GroupM. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at GroupM. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data management and analytics. Be prepared to discuss your past projects, your approach to data engineering challenges, and how you can contribute to the team.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data integration and management.

How to Answer

Discuss the steps involved in ETL, emphasizing how each step contributes to data quality and accessibility for analysis.

Example

“The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. This process is vital as it ensures that data is clean, consistent, and readily available for analysis, which ultimately supports informed decision-making.”

2. What are some common data warehousing solutions you have worked with?

Familiarity with data warehousing solutions is essential for managing large datasets effectively.

How to Answer

Mention specific data warehousing technologies you have experience with, and briefly describe your role in utilizing them.

Example

“I have worked extensively with Snowflake and Redshift for data warehousing. In my previous role, I was responsible for designing the data schema and optimizing queries to improve performance, which significantly reduced the time taken for data retrieval.”

3. How do you ensure data quality in your projects?

Data quality is a critical aspect of data engineering, and interviewers will want to know your strategies for maintaining it.

How to Answer

Discuss specific techniques or tools you use to validate and clean data, as well as any frameworks you follow.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Great Expectations to automate testing. Additionally, I regularly conduct data audits to identify and rectify any discrepancies, ensuring that the data remains reliable for analysis.”

4. Describe your experience with cloud platforms for data engineering.

Cloud platforms are increasingly used for data storage and processing, so familiarity with them is essential.

How to Answer

Highlight your experience with specific cloud services and how you have utilized them in your projects.

Example

“I have worked with AWS and Google Cloud Platform for deploying data pipelines. For instance, I used AWS Lambda for serverless data processing, which allowed for scalable and cost-effective data handling in real-time applications.”

5. Can you explain the concept of data governance and its significance?

Data governance is crucial for ensuring compliance and data integrity, making it a relevant topic in data engineering interviews.

How to Answer

Define data governance and discuss its importance in managing data assets within an organization.

Example

“Data governance refers to the overall management of data availability, usability, integrity, and security. It is significant because it establishes policies and standards that ensure data is accurate and compliant with regulations, which is essential for maintaining trust and accountability in data-driven decisions.”

Problem-Solving and Analytical Skills

1. Describe a challenging data engineering problem you faced and how you resolved it.

This question assesses your problem-solving skills and ability to handle complex situations.

How to Answer

Provide a specific example, detailing the problem, your approach to solving it, and the outcome.

Example

“In a previous project, we faced performance issues with our data pipeline due to high latency. I analyzed the bottlenecks and implemented parallel processing, which improved the data processing speed by 40%, allowing us to meet our reporting deadlines.”

2. How do you approach optimizing SQL queries?

Optimizing SQL queries is a key skill for a Data Engineer, and interviewers will want to know your strategies.

How to Answer

Discuss specific techniques you use to enhance query performance, such as indexing or query restructuring.

Example

“I optimize SQL queries by analyzing execution plans to identify slow-running queries. I often use indexing on frequently queried columns and rewrite complex joins to reduce the overall execution time, which has led to significant performance improvements in my previous projects.”

3. Can you give an example of how you have used Python in your data engineering tasks?

Python is a widely used programming language in data engineering, so be prepared to discuss your experience with it.

How to Answer

Mention specific libraries or frameworks you have used and how they contributed to your projects.

Example

“I frequently use Python with libraries like Pandas and NumPy for data manipulation and analysis. In one project, I developed a data cleaning script that automated the preprocessing of raw data, which saved the team several hours of manual work each week.”

4. How do you handle data migration from legacy systems to new platforms?

Data migration is a common task for Data Engineers, and interviewers will want to know your approach.

How to Answer

Explain your methodology for planning and executing data migrations, including any tools you use.

Example

“I approach data migration by first conducting a thorough assessment of the legacy system to understand the data structure. I then create a detailed migration plan, utilizing tools like Apache NiFi for data transfer, ensuring that data integrity is maintained throughout the process.”

5. What strategies do you use to manage multiple data projects simultaneously?

Time management and prioritization are essential skills for a Data Engineer, especially in a fast-paced environment.

How to Answer

Discuss your organizational methods and any tools you use to keep track of tasks and deadlines.

Example

“I use project management tools like Jira to track progress on multiple projects. I prioritize tasks based on deadlines and impact, and I hold regular check-ins with stakeholders to ensure alignment and address any potential roadblocks early on.”