A.P. Moller - Maersk Data Engineer Interview Questions + Guide in 2025

A.P. Moller - Maersk is a global leader in logistics and shipping, driving innovation and transforming the industry through its cutting-edge solutions.

As a Data Engineer at Maersk, you will be responsible for designing, developing, and maintaining robust data pipelines and architecture that facilitate efficient data processing and analytics across the organization. Your key responsibilities will include building scalable and secure data solutions in a cloud environment, primarily in Azure, while collaborating with data scientists, product teams, and other engineering disciplines to ensure seamless integration and delivery of data products. You will also be expected to identify business requirements, create data extraction and transformation processes, and implement monitoring solutions to provide actionable insights to stakeholders.

Success in this role requires a deep understanding of SQL and Python for data manipulation, a solid grasp of cloud technologies, particularly Azure services, and experience with big data tools like Kafka and Spark. You should be comfortable working with containerization technologies such as Docker and Kubernetes, and possess a strong attention to detail and commitment to best practices in coding and data quality management. Furthermore, a collaborative mindset and the ability to build strong relationships with diverse stakeholders will be essential to thrive in Maersk's dynamic and inclusive work environment.

This guide aims to equip you with the knowledge and insights necessary to prepare effectively for your interview, helping you to showcase your expertise and alignment with Maersk's values and expectations.

The interview process for a Data Engineer at A.P. Moller - Maersk is structured and thorough, designed to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial Screening

The first step involves a phone interview with a recruiter, which usually lasts about 30-45 minutes. During this call, the recruiter will discuss your background, experience, and motivation for applying to Maersk. They will also provide insights into the company culture and the specific expectations for the Data Engineer role. This is an opportunity for you to showcase your enthusiasm for the position and to ask any preliminary questions you may have.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment. This may take the form of a coding challenge or a take-home test that evaluates your proficiency in SQL, Python, and data engineering concepts. The assessment is designed to gauge your ability to design and implement data pipelines, as well as your understanding of cloud technologies, particularly Azure. Candidates should be prepared to demonstrate their problem-solving skills and technical knowledge through practical exercises.

3. Technical Interviews

Successful candidates from the technical assessment will be invited to participate in one or more technical interviews. These interviews are often conducted via video conferencing and may involve multiple interviewers, including team members and technical leads. Expect to answer questions related to data engineering principles, cloud architecture, and specific technologies such as Azure Data Factory, Docker, and big data tools like Spark and Kafka. You may also be asked to solve coding problems in real-time, so be ready to share your screen and write code while explaining your thought process.

4. Behavioral Interview

In addition to technical skills, Maersk places a strong emphasis on cultural fit and teamwork. A behavioral interview will typically follow the technical assessments, where you will be asked about your past experiences, how you handle challenges, and your approach to collaboration. Questions may focus on your ability to work cross-functionally, your experience with agile methodologies, and how you prioritize tasks in a fast-paced environment.

5. Final Interview

The final stage of the interview process often involves a discussion with senior management or the hiring manager. This interview may cover strategic topics, such as how you can contribute to Maersk's data initiatives and your vision for the role. It’s also an opportunity for you to discuss your career goals and how they align with the company’s objectives.

Throughout the process, candidates are encouraged to ask questions about the team dynamics, ongoing projects, and the company’s approach to data engineering and analytics.

As you prepare for your interview, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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

Understand the Company Culture

Maersk values a diverse and inclusive work environment, where innovation and collaboration are encouraged. Familiarize yourself with their core values and how they align with your own. Be prepared to discuss how your personal values and work ethic can contribute to their mission of transforming the logistics and shipping industry. Demonstrating an understanding of their commitment to sustainability and technology will resonate well with the interviewers.

Prepare for Technical Challenges

As a Data Engineer, you can expect to face challenging technical questions during your interview. Brush up on your SQL skills, as this is a critical component of the role. Be ready to solve complex problems related to data extraction, transformation, and loading (ETL) processes. Familiarize yourself with big data tools like Kafka, Hadoop, and Spark, as well as cloud technologies, particularly Azure. Practice coding problems on platforms like LeetCode to sharpen your algorithmic thinking.

Showcase Your Project Experience

Be prepared to discuss your previous projects in detail, especially those that involved building data pipelines or working with cloud environments. Highlight your experience with Python and any relevant libraries (e.g., Pandas, NumPy) that you have used in data engineering tasks. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions clearly.

Emphasize Collaboration and Communication Skills

Maersk operates in a cross-functional environment, so showcasing your ability to work collaboratively with diverse teams is essential. Be ready to discuss how you have effectively communicated technical concepts to non-technical stakeholders in the past. Provide examples of how you have built strong relationships with team members and contributed to a positive team culture.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Prepare examples that demonstrate your resilience, adaptability, and ability to learn from mistakes. Maersk values a growth mindset, so sharing experiences where you took initiative to improve processes or learn new skills will be beneficial.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers about the team dynamics, ongoing projects, and the company’s future direction. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you. Inquire about the tools and technologies the team is currently using and how they approach data governance and quality assurance.

Follow Up Professionally

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention a key point from the interview that resonated with you. This will help keep you top of mind as they make their decision.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at A.P. Moller - Maersk. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data engineering principles, particularly in cloud environments and with big data technologies. Be prepared to discuss your experience with SQL, Python, data pipelines, and cloud services, as well as your approach to collaboration and stakeholder management.

Technical Skills

1. Can you explain the difference between SQL and NoSQL databases?

Understanding the differences between SQL and NoSQL databases is crucial for a Data Engineer, as it impacts how data is stored, retrieved, and managed.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for each type of database. Highlight scenarios where one might be preferred over the other.

Example

“SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible, allowing for unstructured data and horizontal scaling, which is beneficial for handling large volumes of data in real-time applications.”

2. Describe your experience with building data pipelines. What tools have you used?

This question assesses your hands-on experience with data engineering tasks and the tools you are familiar with.

How to Answer

Mention specific tools and technologies you have used, such as Azure Data Factory, Apache Spark, or others. Discuss the types of data pipelines you have built and the challenges you faced.

Example

“I have built data pipelines using Azure Data Factory to automate data movement and transformation. One project involved integrating data from multiple sources, which required careful planning to ensure data quality and consistency throughout the pipeline.”

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

Data quality is critical in data engineering, and interviewers want to know your approach to maintaining it.

How to Answer

Discuss methods you use for data validation, monitoring, and error handling. Mention any tools or frameworks that assist in maintaining data quality.

Example

“I implement data validation checks at various stages of the pipeline to catch errors early. Additionally, I use monitoring tools to track data quality metrics and set up alerts for any anomalies, ensuring that stakeholders are informed of potential issues.”

4. Can you explain the concept of ETL and how it differs from ELT?

Understanding ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) is essential for data engineers, especially in cloud environments.

How to Answer

Define both concepts and explain the scenarios in which each is used, particularly in relation to cloud data processing.

Example

“ETL involves transforming data before loading it into the target system, which is useful for structured data. ELT, on the other hand, loads raw data into the target system first and then transforms it, which is more efficient for large datasets in cloud environments where storage is less of a concern.”

Cloud Technologies

5. What is your experience with Azure services, particularly Azure Data Factory?

This question assesses your familiarity with cloud technologies, specifically those used at Maersk.

How to Answer

Discuss your experience with Azure services, focusing on Azure Data Factory and how you have utilized it in your projects.

Example

“I have extensive experience with Azure Data Factory, where I designed and implemented data integration workflows to move data between various Azure services. This included setting up triggers and monitoring the performance of data pipelines to ensure timely data availability.”

6. How do you handle version control in your data engineering projects?

Version control is vital for collaboration and maintaining code quality in data engineering.

How to Answer

Explain the tools you use for version control, such as Git, and how you manage changes in your data engineering projects.

Example

“I use Git for version control, ensuring that all changes to the codebase are tracked. I follow best practices by creating branches for new features and conducting code reviews before merging changes to the main branch, which helps maintain code quality and collaboration among team members.”

Problem-Solving and Collaboration

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

This question evaluates your problem-solving skills and ability to handle challenges in data engineering.

How to Answer

Provide a specific example of a problem you encountered, the steps you took to resolve it, and the outcome.

Example

“In a previous project, I faced issues with data latency due to inefficient data processing. I analyzed the pipeline and identified bottlenecks in the transformation stage. By optimizing the code and leveraging parallel processing with Apache Spark, I reduced the processing time by 50%, significantly improving data availability for stakeholders.”

8. How do you communicate technical concepts to non-technical stakeholders?

Effective communication is key in a collaborative environment, especially when working with diverse teams.

How to Answer

Discuss your approach to simplifying complex technical concepts and ensuring that stakeholders understand the implications for their work.

Example

“I focus on using analogies and visual aids to explain technical concepts. For instance, when discussing data pipelines, I compare them to water pipes, explaining how data flows through different stages. This helps non-technical stakeholders grasp the importance of data quality and processing times in their decision-making.”

9. How do you prioritize tasks when working on multiple data engineering projects?

This question assesses your time management and prioritization skills in a fast-paced environment.

How to Answer

Explain your approach to prioritizing tasks based on project deadlines, stakeholder needs, and the complexity of tasks.

Example

“I prioritize tasks by assessing their impact on project timelines and stakeholder requirements. I use project management tools to track progress and ensure that I allocate time effectively, focusing on high-impact tasks first while keeping communication open with stakeholders about any changes in priorities.”

10. Can you give an example of how you have worked cross-functionally with other teams?

Collaboration across teams is essential in data engineering, and interviewers want to know about your experience in this area.

How to Answer

Provide an example of a project where you collaborated with other teams, highlighting the importance of teamwork and communication.

Example

“In a recent project, I collaborated with data scientists and product managers to develop a new data product. I facilitated regular meetings to ensure alignment on requirements and timelines, which helped us deliver the product on schedule and meet the needs of our users effectively.”