Marsh Data Engineer Interview Questions + Guide in 2025

Marsh is a leading global professional services firm that provides business insurance, employee health & benefits, retirement, and private client insurance solutions to organizations and individuals.

As a Data Engineer at Marsh, you will play a critical role in designing, building, and maintaining data pipelines and systems that facilitate data-driven decision-making across the organization. Your primary responsibilities will include developing scalable ETL processes, optimizing data models, and implementing best practices for data management and governance. You will collaborate closely with cross-functional teams, leveraging your expertise in cloud technologies such as Azure, to ensure that data is accessible, reliable, and secure.

Ideal candidates will possess a strong background in data engineering, with proficiency in programming languages such as Python and SQL, as well as experience with data warehousing solutions. You should be comfortable navigating complex data landscapes and have a solid understanding of data architecture principles. A passion for problem-solving and the ability to communicate technical concepts to non-technical stakeholders are essential traits that will set you apart in this role.

This guide will provide you with tailored insights and preparation strategies to excel during your interview at Marsh, ensuring you are well-equipped to demonstrate your fit for the Data Engineer position.

The interview process for a Data Engineer at Marsh is structured to assess both technical skills and cultural fit within the organization. It typically unfolds over several stages, allowing candidates to showcase their expertise and alignment with Marsh's values.

1. Application and Initial Screening

The process begins with an online application, where candidates submit their resumes and cover letters. Following this, a recruiter conducts a brief phone screening. This initial conversation focuses on the candidate's background, motivations for applying to Marsh, and basic qualifications for the role. Expect general questions about your experience and interest in the company.

2. Recorded Video Interview

Candidates who pass the initial screening may be invited to complete a recorded video interview. This step can feel a bit intimidating, as it involves answering a set of pre-defined questions within a limited time frame. The questions are tailored to the position and often include problem-solving scenarios relevant to data engineering. This format allows candidates to demonstrate their thought processes and technical knowledge.

3. HR Interview

Successful candidates from the video interview will then have a one-on-one interview with an HR representative. This conversation is typically more relaxed and focuses on behavioral questions. Expect inquiries about past experiences, challenges faced, and how you overcame them. The HR interview also assesses cultural fit and communication skills, which are crucial for collaboration within teams.

4. Technical Interview

The next step is a technical interview, which may involve one or more rounds with hiring managers or senior data engineers. This interview dives deeper into your technical expertise, including your experience with data modeling, ETL processes, and cloud technologies. Candidates should be prepared to discuss specific projects they've worked on, as well as demonstrate their problem-solving abilities through technical questions or coding exercises.

5. Final Interview

In some cases, candidates may have a final interview with senior leadership or team members. This round often includes discussions about long-term goals, team dynamics, and how the candidate envisions contributing to Marsh's data initiatives. It’s an opportunity for both parties to assess mutual fit and alignment with the company’s mission.

Throughout the process, candidates are encouraged to ask questions about the team, projects, and company culture to ensure that Marsh is the right fit for them as well.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical skills and past experiences.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Marsh. The interview process will likely focus on your technical skills, problem-solving abilities, and how well you can communicate complex ideas. Be prepared to discuss your experience with data architecture, cloud technologies, and your approach to data management.

Technical Skills

1. Can you explain the differences between OLTP and OLAP systems?

Understanding the distinctions between these systems is crucial for a Data Engineer, as they serve different purposes in data management.

How to Answer

Discuss the characteristics of both systems, emphasizing their use cases, performance metrics, and how they handle data.

Example

“OLTP systems are designed for transaction-oriented tasks, focusing on fast query processing and maintaining data integrity in multi-user environments. In contrast, OLAP systems are optimized for complex queries and data analysis, allowing for quick retrieval of aggregated data for reporting and decision-making.”

2. Describe your experience with ETL processes. What tools have you used?

ETL (Extract, Transform, Load) processes are fundamental in data engineering, and familiarity with various tools is essential.

How to Answer

Mention specific ETL tools you have used, your role in the ETL process, and any challenges you faced.

Example

“I have extensive experience with Azure Data Factory for ETL processes. In my previous role, I designed and implemented data pipelines that extracted data from various sources, transformed it to meet business requirements, and loaded it into our data warehouse. One challenge I faced was optimizing the transformation process to reduce latency, which I addressed by implementing parallel processing.”

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

Data quality is critical for any data-driven organization, and your approach to maintaining it will be scrutinized.

How to Answer

Discuss the methods and tools you use to validate data, monitor data quality, and handle discrepancies.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Azure Data Factory to automate these checks. Additionally, I regularly conduct data audits and use data profiling techniques to identify and rectify any anomalies, ensuring that the data remains accurate and reliable.”

4. Can you explain your experience with cloud database technologies?

Given the emphasis on cloud solutions, your familiarity with cloud databases will be a key topic.

How to Answer

Highlight your experience with specific cloud platforms and databases, and discuss how you have leveraged them in your projects.

Example

“I have worked extensively with Azure SQL Database and Azure Cosmos DB. In my last project, I migrated our on-premises database to Azure SQL, which improved our scalability and reduced costs. I also utilized Cosmos DB for a real-time analytics application, taking advantage of its multi-model capabilities.”

5. What strategies do you use for optimizing database performance?

Performance optimization is a critical skill for a Data Engineer, and interviewers will want to know your strategies.

How to Answer

Discuss specific techniques you have used to enhance performance, such as indexing, query optimization, or partitioning.

Example

“I focus on indexing frequently queried columns and regularly analyze query performance using execution plans. In one instance, I identified a slow-running query and optimized it by rewriting it to reduce the number of joins, which significantly improved response times.”

Behavioral Questions

1. Describe a challenging project you worked on and how you overcame the obstacles.

This question assesses your problem-solving skills and resilience in the face of challenges.

How to Answer

Choose a specific project, outline the challenges you faced, and explain the steps you took to overcome them.

Example

“In a previous project, we faced significant delays due to data inconsistencies from multiple sources. I organized a series of workshops with stakeholders to identify the root causes and implemented a data governance framework that established clear data standards and ownership, which ultimately streamlined our processes.”

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

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

How to Answer

Discuss your approach to prioritization, including any tools or methodologies you use.

Example

“I use a combination of Agile methodologies and project management tools like Jira to prioritize tasks based on urgency and impact. I regularly communicate with my team to reassess priorities and ensure alignment with project goals, which helps me stay focused on delivering high-quality results.”

3. How do you handle feedback and criticism?

Your ability to accept and act on feedback is crucial for personal and professional growth.

How to Answer

Share your perspective on feedback and provide an example of how you have used it to improve.

Example

“I view feedback as an opportunity for growth. For instance, after receiving constructive criticism on my presentation skills, I took a public speaking course and sought opportunities to present more frequently. This not only improved my skills but also boosted my confidence in sharing my work with stakeholders.”

4. Can you give an example of how you communicated complex technical information to a non-technical audience?

Effective communication is vital, especially when working with cross-functional teams.

How to Answer

Describe a specific instance where you successfully conveyed technical information in an understandable way.

Example

“I once had to present a data migration plan to a group of non-technical stakeholders. I created visual aids and simplified the technical jargon, focusing on the benefits and impact of the migration rather than the technical details. This approach helped them understand the project’s importance and gain their support.”

5. What motivates you to work in data engineering?

Understanding your motivation can help interviewers gauge your fit within the company culture.

How to Answer

Share your passion for data engineering and what aspects of the role excite you the most.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. The ability to solve complex problems and contribute to data-driven decision-making is incredibly fulfilling for me. I enjoy staying updated with the latest technologies and trends in data engineering, which keeps my work exciting and relevant.”