Marsh & McLennan Companies Data Engineer Interview Questions + Guide in 2025

Marsh & McLennan Companies is a global leader in professional services, providing clients with innovative solutions in business insurance, employee health & benefits, and retirement planning.

As a Data Engineer at Marsh & McLennan, you will play a critical role in building and maintaining robust data architectures and pipelines that facilitate data-driven decision-making across the organization. Key responsibilities include designing, constructing, and optimizing data systems and pipelines, as well as developing ETL processes to ensure the efficient flow of data from various sources into data storage solutions. You will work closely with data scientists, analysts, and stakeholders to deliver high-quality data for analytics and reporting purposes. Proficiency in cloud-based data technologies, especially within the Azure ecosystem, is essential, as is experience with SQL and NoSQL databases.

Success in this role requires a combination of technical expertise, analytical skills, and effective communication abilities, particularly when translating complex technical concepts to non-technical stakeholders. Familiarity with Agile methodologies and the ability to collaborate in cross-functional teams will also enhance your impact within the organization.

This guide will equip you with the knowledge and insights necessary to excel in your interview, helping you demonstrate your fit for the Data Engineer role at Marsh & McLennan.

The interview process for a Data Engineer role at Marsh & McLennan Companies is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's values and technical requirements. Typically, the process consists of three main rounds, each designed to evaluate different aspects of a candidate's qualifications and fit for the role.

1. Initial Screening

The first step in the interview process is an initial screening, usually conducted by a recruiter. This is a brief phone interview where the recruiter will discuss your background, experience, and interest in the Data Engineer position. They will also provide insights into the company culture and the specifics of the role. This conversation is crucial for determining if you meet the basic qualifications and if you are a good cultural fit for Marsh & McLennan.

2. Technical Interviews

Following the initial screening, candidates typically undergo two technical interviews. These interviews may be conducted virtually or in-person and focus on assessing your technical expertise in data engineering. Expect to encounter questions related to data modeling, ETL processes, and cloud technologies, particularly those relevant to Azure. You may also be asked to solve coding problems or case studies that demonstrate your problem-solving abilities and understanding of data architecture principles. The interviewers will likely include members of the technical team, such as data engineers or architects, who will evaluate your technical skills and your approach to real-world data challenges.

3. Final Interview

The final round usually involves a more comprehensive interview with senior management or team leads. This interview may include both technical and behavioral questions, allowing the interviewers to gauge your soft skills, such as communication and teamwork, alongside your technical capabilities. You may be asked to present a case study or discuss your previous projects in detail, highlighting your role and the impact of your work. This round is also an opportunity for you to ask questions about the team dynamics, project expectations, and the company's vision for data engineering.

As you prepare for your interviews, it's essential to be ready for a variety of questions that will test both your technical knowledge and your ability to work collaboratively within a team.

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

Understand the Interview Structure

The interview process at Marsh & McLennan typically consists of three rounds: two technical interviews followed by an HR round. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your technical skills in depth, as well as your experience with agile project delivery, which is a key focus for the company. Knowing the format will help you manage your time and responses effectively.

Prepare for Technical Challenges

Given the emphasis on technical skills, ensure you are well-versed in relevant technologies such as Azure, SQL, and data modeling. Brush up on your coding skills and be ready to tackle practical problems, as candidates have reported technical discussions and coding questions that test your problem-solving abilities. Practice common data engineering scenarios and be prepared to explain your thought process clearly.

Showcase Your Experience with Data Solutions

Be ready to discuss your past experiences in designing and implementing data solutions. Highlight specific projects where you have successfully built scalable data pipelines or improved data models. Use the STAR (Situation, Task, Action, Result) method to structure your responses, making it easier for interviewers to understand your contributions and the impact of your work.

Communicate Effectively

Strong communication skills are essential, especially when discussing complex technical concepts. Be prepared to explain your ideas in a way that is accessible to non-technical stakeholders. This is particularly important at Marsh & McLennan, where collaboration across teams is crucial. Practice articulating your thoughts clearly and concisely, and be ready to engage in discussions about how your work aligns with business objectives.

Embrace the Company Culture

Marsh & McLennan prides itself on its inclusive and collaborative culture. Demonstrate your alignment with these values by sharing examples of how you have worked effectively in teams and contributed to a positive work environment. Show enthusiasm for the company’s mission and values, and express your desire to be part of a team that makes a meaningful impact.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Reflect on your past experiences and be ready to discuss how you have navigated difficult situations, managed conflicts, or adapted to changes in project scope. This will help interviewers gauge your fit within the team and the organization.

Follow Up with Questions

At the end of the interview, take the opportunity to ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you determine if Marsh & McLennan is the right fit for you. Consider asking about the company’s approach to data governance or how they foster collaboration among teams.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Marsh & McLennan. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Marsh & McLennan Companies. The interview process will likely focus on your technical skills, problem-solving abilities, and your experience with data architecture and engineering practices. Be prepared to discuss your past projects, methodologies, and how you approach data challenges.

Technical Skills

1. Can you describe your experience with Azure Data Factory and how you have used it in your projects?

Understanding your hands-on experience with Azure Data Factory is crucial, as it is a key tool for data integration in the cloud.

How to Answer

Discuss specific projects where you utilized Azure Data Factory, focusing on the challenges you faced and how you overcame them.

Example

“In my previous role, I used Azure Data Factory to automate the ETL process for a large-scale data migration project. I designed data pipelines that integrated data from various sources, ensuring data quality and consistency. This not only improved our data processing time by 30% but also enhanced the accuracy of our analytics.”

2. What strategies do you use for optimizing SQL queries?

Optimization is essential for performance, especially when dealing with large datasets.

How to Answer

Explain your approach to identifying slow queries and the techniques you employ to enhance their performance.

Example

“I typically start by analyzing the execution plan to identify bottlenecks. I then focus on indexing strategies, rewriting queries to reduce complexity, and ensuring that I’m using the most efficient joins. For instance, in a recent project, I reduced query execution time by 50% by implementing proper indexing and restructuring the query logic.”

3. How do you ensure data quality and integrity in your data pipelines?

Data quality is paramount in data engineering, and interviewers will want to know your methods for maintaining it.

How to Answer

Discuss the tools and processes you use to monitor and validate data quality throughout the pipeline.

Example

“I implement data validation checks at various stages of the pipeline, using tools like Azure Data Factory’s built-in monitoring features. Additionally, I create automated tests that run after data ingestion to ensure that the data meets predefined quality standards, which has significantly reduced errors in our reporting.”

4. Describe a challenging data architecture problem you faced and how you solved it.

This question assesses your problem-solving skills and technical expertise.

How to Answer

Provide a specific example that highlights your analytical skills and technical knowledge.

Example

“In a previous project, we faced issues with data redundancy across multiple systems. I proposed a centralized data lake architecture that allowed us to consolidate data from various sources. By implementing this solution, we not only reduced redundancy but also improved data accessibility for analytics, leading to better decision-making.”

Behavioral Questions

1. How do you prioritize tasks when working on multiple projects?

Time management is critical in a fast-paced environment.

How to Answer

Explain your approach to prioritization and how you communicate with stakeholders.

Example

“I use a combination of project management tools and regular check-ins with stakeholders to prioritize tasks. I assess the urgency and impact of each task, ensuring that I focus on high-priority items first. This approach has helped me meet deadlines consistently while maintaining quality.”

2. Can you give an example of how you handled a conflict with a team member?

Collaboration is key in data engineering roles, and conflict resolution skills are essential.

How to Answer

Share a specific instance where you navigated a conflict and the outcome.

Example

“During a project, a colleague and I disagreed on the approach to data modeling. I suggested we hold a meeting to discuss our perspectives and find common ground. By facilitating an open dialogue, we were able to merge our ideas into a more robust solution that satisfied both our concerns.”

3. How do you stay updated with the latest trends and technologies in data engineering?

Continuous learning is vital in the tech industry.

How to Answer

Discuss the resources you use to keep your skills sharp and your knowledge current.

Example

“I regularly attend webinars and workshops, and I’m an active member of several online data engineering communities. I also follow industry leaders on platforms like LinkedIn and read relevant blogs and publications to stay informed about emerging technologies and best practices.”

4. Why are you interested in working for Marsh & McLennan Companies?

This question assesses your motivation and alignment with the company’s values.

How to Answer

Express your interest in the company’s mission and how it aligns with your career goals.

Example

“I admire Marsh & McLennan’s commitment to innovation and its focus on delivering meaningful impact. I believe that my skills in data engineering can contribute to your mission of providing exceptional service to clients, and I’m excited about the opportunity to work in such a collaborative and inclusive environment.”