Merck Data Engineer Interview Questions + Guide in 2025

Merck is a global healthcare company committed to improving lives through innovative medicines, vaccines, and animal health products.

As a Data Engineer at Merck, you will play a crucial role in the Scientific Data Consumption Product Line, where you will collaborate with cross-functional teams to design, develop, and maintain data products that enable advanced analytics and AI/ML applications. Your responsibilities will include building and optimizing data pipelines, transforming complex research data into accessible formats, and ensuring data quality through governance practices. The ideal candidate will have a strong foundation in cloud services, particularly AWS, and a proficiency in programming languages such as Python or Java. You will also be expected to work in agile teams, applying best practices in data engineering, including test-driven development and version control with Git.

This guide will prepare you for your interview by highlighting the key competencies and experiences that Merck values in a Data Engineer, thereby helping you to articulate your fit for the role confidently.

The interview process for a Data Engineer position at Merck is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and innovative environment of the company.

1. Initial Screening

The process begins with an initial screening, typically conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on understanding your background, skills, and motivations. The recruiter will provide insights into Merck's work culture and expectations, while also gauging your fit for the role and the company.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted through a video call. This stage involves a series of technical questions that evaluate your proficiency in relevant programming languages such as Python or Java, as well as your experience with AWS services and data engineering tools. Expect to discuss your previous projects and how you approached complex data challenges, as well as your familiarity with data pipeline development and orchestration tools.

3. Behavioral Interview

Candidates will then participate in a behavioral interview, which focuses on your past experiences and how they align with Merck's values. This interview will explore your ability to work in cross-functional teams, your problem-solving skills, and your approach to collaboration. Be prepared to share specific examples that demonstrate your teamwork and adaptability in various situations.

4. Onsite Interview

The final stage is an onsite interview, which may consist of multiple rounds with different team members, including data engineers, product analysts, and possibly stakeholders. Each session will delve deeper into your technical expertise, data modeling capabilities, and understanding of data governance. Additionally, you may be asked to participate in a practical exercise or case study that simulates real-world data engineering challenges at Merck.

As you prepare for these interviews, it's essential to familiarize yourself with the specific tools and technologies mentioned in the job description, as well as to reflect on your past experiences that showcase your skills and fit for the role.

Next, let's explore the types of interview questions you might encounter during this process.

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

Understand the Company Culture

Merck values professionalism and teamwork, so it’s essential to demonstrate your ability to collaborate effectively. Familiarize yourself with their working processes and be prepared to discuss how you can contribute to a team-oriented environment. Highlight experiences where you successfully worked in cross-functional teams, as this will resonate well with the interviewers.

Prepare for Behavioral Questions

Expect a mix of behavioral and technical questions during your interview. Prepare to share specific examples from your past experiences that showcase your problem-solving skills, adaptability, and ability to work under pressure. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your contributions clearly.

Showcase Your Technical Expertise

Given the technical nature of the Data Engineer role, be ready to discuss your experience with AWS services, data pipeline development, and relevant programming languages like Python and Java. Brush up on your knowledge of tools such as Jenkins, Terraform, and Spark, and be prepared to explain how you have used them in previous projects. Demonstrating a solid understanding of data governance and quality checks will also be beneficial.

Emphasize Your Problem-Solving Skills

Merck is focused on solving complex data problems, especially in the context of scientific data. Be prepared to discuss how you approach problem-solving, particularly in scenarios involving data transformation and analytics readiness. Share examples of how you have tackled similar challenges in the past and the methodologies you employed, such as test-driven development.

Ask Insightful Questions

At the end of the interview, take the opportunity to ask thoughtful questions that reflect your interest in the role and the company. Inquire about the specific challenges the team is currently facing, the tools they use for data orchestration, or how they measure the success of their data products. This not only shows your enthusiasm but also helps you gauge if the company aligns with your career goals.

Be Yourself

While it’s important to prepare and present your best self, don’t forget to be authentic. Merck appreciates candidates who are genuine and can bring their unique perspectives to the team. Let your personality shine through in your responses, and don’t hesitate to share your passion for data engineering and its impact on scientific advancements.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Merck. The interview will likely focus on your technical skills, experience with data engineering tools, and your ability to work collaboratively in cross-functional teams. Be prepared to discuss your previous projects and how they relate to the responsibilities outlined in the job description.

Technical Skills

1. Can you explain your experience with AWS services, particularly S3 and Redshift?

Understanding AWS services is crucial for this role, as they are integral to data storage and processing.

How to Answer

Discuss specific projects where you utilized these services, focusing on the challenges faced and how you overcame them.

Example

“In my previous role, I used S3 for data storage and Redshift for data warehousing. I designed a data pipeline that ingested large datasets into S3, transformed them using AWS Glue, and then loaded them into Redshift for analytics. This setup improved our query performance by 30%.”

2. Describe your experience with data pipeline development. What tools have you used?

This question assesses your hands-on experience with data engineering tools and methodologies.

How to Answer

Mention specific tools and frameworks you have used, and provide examples of how you implemented them in your projects.

Example

“I have developed data pipelines using Apache Spark and AWS Glue. For instance, I built a pipeline that extracted data from various sources, transformed it using PySpark, and loaded it into a data lake. This process was automated using AWS Lambda, ensuring timely data availability for our analytics team.”

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

Data quality is critical in data engineering, and this question evaluates your approach to maintaining it.

How to Answer

Discuss the methods and tools you use to implement data quality checks and how you handle data discrepancies.

Example

“I implement data quality checks at various stages of the ETL process. For example, I use AWS Glue to validate data types and ranges during the transformation phase. Additionally, I set up monitoring alerts in CloudWatch to notify the team of any anomalies in the data.”

4. Can you explain your experience with version control systems, particularly Git?

Version control is essential for collaborative work, and this question gauges your familiarity with it.

How to Answer

Share your experience with Git, including branching strategies and collaboration with team members.

Example

“I have used Git extensively for version control in my projects. I follow a feature branching strategy, where each new feature is developed in its own branch. This allows for easier code reviews and integration. I also ensure to write clear commit messages to maintain a clean project history.”

5. What is your experience with orchestration tools?

Orchestration tools are vital for managing complex data workflows, and this question assesses your knowledge in this area.

How to Answer

Discuss specific orchestration tools you have used and how they contributed to your data engineering processes.

Example

“I have experience using Apache Airflow for orchestrating data workflows. I designed DAGs to automate the ETL processes, ensuring that tasks were executed in the correct order and handling retries for failed tasks. This significantly improved the reliability of our data pipelines.”

Collaboration and Problem-Solving

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

This question evaluates your problem-solving skills and ability to work under pressure.

How to Answer

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

Example

“In a previous project, we faced issues with data latency due to slow processing times. I analyzed the pipeline and identified bottlenecks in the transformation stage. By optimizing our Spark jobs and implementing partitioning strategies, we reduced processing time by 50%, which improved our data availability for analysis.”

2. How do you approach working with cross-functional teams?

Collaboration is key in this role, and this question assesses your interpersonal skills.

How to Answer

Discuss your experience working with different teams and how you ensure effective communication.

Example

“I believe in maintaining open lines of communication with cross-functional teams. In my last project, I held regular meetings with data scientists and product managers to align on data requirements and project goals. This collaboration helped us deliver a data product that met everyone’s needs.”

3. Can you give an example of how you documented a data engineering process?

Documentation is important for knowledge sharing and project continuity.

How to Answer

Share your approach to documentation and provide an example of a process you documented.

Example

“I always document my data engineering processes to ensure clarity for future team members. For instance, I created a comprehensive guide for our ETL pipeline, detailing each step, the tools used, and troubleshooting tips. This documentation was invaluable for onboarding new team members and maintaining the pipeline.”

4. How do you handle feedback from team members or stakeholders?

This question assesses your receptiveness to feedback and your ability to adapt.

How to Answer

Discuss your approach to receiving and implementing feedback in your work.

Example

“I view feedback as an opportunity for growth. When I receive feedback, I take the time to understand the perspective of the person providing it. For example, after a code review, I implemented suggestions to improve the efficiency of my data pipeline, which ultimately enhanced our project outcomes.”

5. Describe a time when you had to learn a new technology quickly. How did you approach it?

This question evaluates your adaptability and willingness to learn.

How to Answer

Share a specific instance where you had to learn a new technology and how you managed the learning process.

Example

“When I needed to learn Databricks for a project, I dedicated time to online courses and hands-on practice. I also reached out to colleagues who had experience with the platform for guidance. Within a few weeks, I was able to successfully implement a data processing solution using Databricks, which contributed to our project’s success.”