FM Global Data Engineer Interview Questions + Guide in 2025

FM Global is a leading property insurer of the world's largest businesses, providing innovative engineering-based risk management and property insurance solutions to a diverse clientele.

As a Data Engineer at FM Global, you will play a pivotal role in developing and maintaining robust data infrastructure to support the company’s advanced analytics capabilities. Your key responsibilities will include designing and implementing scalable data pipelines, ensuring data quality and compliance, and collaborating closely with data scientists and other stakeholders to seamlessly integrate machine learning models into production. You will leverage your expertise in SQL, Spark SQL, and programming languages such as Python and R, along with tools like Databricks and MLflow, to drive efficiencies in data processing and improve model deployment velocity. An ideal candidate will possess a strong background in MLOps, data engineering, and software development, demonstrating problem-solving skills and the ability to communicate effectively with both technical and non-technical teams.

This guide will equip you with a clear understanding of the expectations and requirements for the Data Engineer role at FM Global, helping you to prepare effectively for your interview and stand out as a qualified candidate.

The interview process for the Data Engineer role at FM Global is structured to assess both technical expertise and cultural fit within the organization. Candidates can expect a multi-step process that evaluates their skills in data engineering, machine learning operations, and collaboration with cross-functional teams.

1. Initial Screening

The first step in the interview process is an initial screening conducted by a recruiter. This typically lasts about 30 minutes and focuses on understanding the candidate's background, experience, and motivations for applying to FM Global. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a coding challenge or a technical interview. This assessment will focus on key skills such as SQL, Spark SQL, and Python programming. Candidates should be prepared to demonstrate their ability to design data pipelines, implement ETL processes, and solve data-related problems. Familiarity with tools like Databricks and MLflow may also be evaluated.

3. Behavioral Interview

Candidates who pass the technical assessment will be invited to a behavioral interview. This round typically involves multiple interviewers, including team members and managers. The focus here is on assessing soft skills, such as problem-solving abilities, communication skills, and teamwork. Candidates should be ready to discuss past experiences where they collaborated with cross-functional teams, handled challenges, and contributed to project success.

4. Onsite Interview (or Virtual Onsite)

The final stage of the interview process is an onsite interview, which may also be conducted virtually. This round usually consists of several one-on-one interviews with various stakeholders, including data scientists, engineers, and product managers. Candidates will be asked to tackle real-world scenarios related to data engineering and machine learning operations, demonstrating their technical knowledge and ability to work in a collaborative environment.

Throughout the interview process, candidates should emphasize their experience with cloud platforms (especially Azure), data versioning tools, and their understanding of data privacy and security best practices.

As you prepare for your interview, consider the specific questions that may arise in each of these stages.

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

Understand the Role and Its Impact

Before your interview, take the time to deeply understand the responsibilities of a Data Engineer at FM Global. This role is not just about coding; it involves building robust data platforms and pipelines that enable advanced analytics. Familiarize yourself with how your work will directly impact the company's ability to provide engineering-based risk management and property insurance solutions. This understanding will allow you to articulate how your skills align with the company's mission.

Master the Technical Skills

Given the emphasis on SQL and algorithms, ensure you are well-versed in these areas. Brush up on your SQL skills, focusing on complex queries, data manipulation, and performance optimization. Additionally, practice algorithmic thinking, as you may be asked to solve problems that require a solid understanding of data structures and algorithms. Familiarity with Spark SQL and PySpark will also be crucial, so be prepared to discuss your experience with these technologies.

Showcase Your MLOps Knowledge

Since the role involves Machine Learning Operations, be ready to discuss your experience with MLOps practices. Highlight any projects where you have implemented CI/CD pipelines for machine learning workflows, as well as your familiarity with tools like MLflow for model tracking and deployment. If you have experience with Azure, especially in the context of MLOps, make sure to emphasize this, as it is a key requirement for the role.

Prepare for Cross-Functional Collaboration

The role requires collaboration with various teams, including Data Science, Solution Architecture, and business stakeholders. Be prepared to discuss your experience working in cross-functional teams and how you effectively communicate technical concepts to non-technical stakeholders. Highlight any instances where you successfully navigated differing priorities or resolved conflicts within a team setting.

Emphasize Problem-Solving Skills

FM Global values strong problem-solving abilities. Prepare to share specific examples of challenges you've faced in previous roles and how you approached them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate the impact of your solutions.

Familiarize Yourself with Company Culture

FM Global prides itself on a dynamic and culturally diverse workforce. Research the company's values and culture, and think about how your personal values align with theirs. Be ready to discuss how you can contribute to a collaborative and inclusive work environment.

Ask Insightful Questions

Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about the team dynamics, the tools and technologies they use, and how success is measured in the Data Engineering team. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

By following these tips, you will be well-prepared to showcase your skills and fit for the Data Engineer role at FM Global. Good luck!

FM Global Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at FM Global. The interview will focus on your technical skills in data engineering, machine learning operations, and your ability to work collaboratively in a cross-functional team. Be prepared to demonstrate your knowledge of data pipelines, cloud technologies, and programming languages relevant to the role.

Technical Skills

1. Can you explain the process of building a data pipeline from scratch?

This question assesses your understanding of data pipeline architecture and your practical experience in building one.

How to Answer

Outline the steps involved in designing, implementing, and maintaining a data pipeline, including data ingestion, transformation, and storage. Mention any tools or technologies you have used.

Example

“To build a data pipeline, I start by identifying the data sources and determining the required transformations. I then use tools like Azure Data Factory for ingestion and ETL processes, ensuring data quality through validation checks. Finally, I store the processed data in a cloud database like Azure SQL Database, making it accessible for analytics.”

2. What is your experience with SQL and how do you optimize queries?

This question evaluates your SQL proficiency and your ability to enhance performance.

How to Answer

Discuss your experience with SQL, including specific functions or techniques you use to optimize queries, such as indexing or query restructuring.

Example

“I have extensive experience with SQL, particularly in writing complex queries for data extraction. To optimize performance, I often use indexing on frequently queried columns and analyze execution plans to identify bottlenecks. Additionally, I rewrite queries to minimize joins and subqueries when possible.”

3. Describe your experience with cloud platforms, specifically Azure.

This question gauges your familiarity with cloud technologies and their application in data engineering.

How to Answer

Share your experience with Azure services, focusing on specific tools like Azure Data Factory, Azure SQL Database, or Azure Databricks.

Example

“I have worked extensively with Azure, particularly with Azure Data Factory for orchestrating data workflows and Azure Databricks for processing large datasets using Spark. I also utilize Azure SQL Database for storing structured data and ensuring high availability.”

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

This question assesses your approach to maintaining data quality throughout the data lifecycle.

How to Answer

Discuss the methods you use to validate and cleanse data, as well as any tools or frameworks that assist in this process.

Example

“To ensure data quality, I implement validation checks at various stages of the data pipeline. I use data profiling techniques to identify anomalies and apply cleansing methods to rectify issues. Additionally, I maintain comprehensive documentation to track data lineage and transformations.”

5. Can you explain the concept of ETL vs. ELT? When would you use one over the other?

This question tests your understanding of data processing methodologies.

How to Answer

Define ETL and ELT, and explain scenarios where one might be preferred over the other based on data volume, processing speed, and use cases.

Example

“ETL stands for Extract, Transform, Load, where data is transformed before loading into the target system. ELT, on the other hand, loads raw data first and transforms it afterward. I prefer ELT for large datasets where processing power is available in the target system, as it allows for more flexibility in data analysis.”

Machine Learning Operations

1. What is your experience with MLOps, and how do you implement it in your projects?

This question evaluates your understanding of machine learning operations and their integration into data engineering.

How to Answer

Discuss your experience with MLOps practices, including model deployment, monitoring, and collaboration with data scientists.

Example

“I have implemented MLOps by establishing CI/CD pipelines for deploying machine learning models. I use tools like MLflow for tracking experiments and managing model versions. Additionally, I set up monitoring systems to track model performance and data drift post-deployment.”

2. How do you handle versioning of data and models in your projects?

This question assesses your approach to managing changes in data and models over time.

How to Answer

Explain your strategies for version control, including tools and practices you use to ensure reproducibility and traceability.

Example

“I use Git for versioning code and MLflow for tracking model versions. For data, I implement data versioning tools like DVC to manage changes in datasets. This ensures that I can reproduce results and maintain a clear history of changes.”

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

This question allows you to showcase your problem-solving skills and technical expertise.

How to Answer

Provide a specific example of a challenge, the steps you took to address it, and the outcome.

Example

“I once faced a challenge with data ingestion from multiple sources that had inconsistent formats. I developed a custom transformation layer using Python to standardize the data before loading it into our data warehouse. This not only improved data quality but also streamlined our ETL process.”

4. What tools do you use for monitoring and alerting in production ML systems?

This question assesses your knowledge of operational monitoring in machine learning environments.

How to Answer

Discuss the tools and techniques you use to monitor model performance and set up alerts for anomalies.

Example

“I use tools like Prometheus and Grafana for monitoring model performance metrics. I also set up alerts using Azure Monitor to notify the team of any significant deviations in model predictions or data drift, allowing us to take corrective actions promptly.”

5. How do you collaborate with data scientists and other stakeholders in your projects?

This question evaluates your teamwork and communication skills in a cross-functional environment.

How to Answer

Describe your approach to collaboration, including communication methods and tools you use to ensure alignment.

Example

“I prioritize regular communication with data scientists and stakeholders through weekly stand-ups and collaborative tools like Jira and Confluence. This ensures that everyone is aligned on project goals and timelines, and it allows us to address any challenges collaboratively.”