Glatfelter Data Engineer Interview Questions + Guide in 2025

Glatfelter is a global supplier of engineered materials, providing innovative solutions to customers across diverse markets.

As a Data Engineer at Glatfelter, you will play a critical role in developing and maintaining robust data pipelines and architectures that are essential for data-driven decision-making. Key responsibilities include designing, implementing, and optimizing data storage solutions, ensuring data quality and integrity, and collaborating with data scientists and analysts to facilitate data accessibility for analytical purposes. You will also be expected to engage in data modeling, ETL processes, and the integration of new data sources while adhering to best practices in data governance and security.

Successful candidates will possess a strong technical background in programming languages such as Python or Java, and proficiency with data management tools like SQL, Spark, or Hadoop. Experience with cloud platforms (e.g., AWS, Azure) is highly valued. Additionally, strong problem-solving skills, an analytical mindset, and the ability to work collaboratively within a team-oriented environment are essential traits that align with Glatfelter's commitment to innovation and excellence.

This guide will help you prepare for a job interview by equipping you with a deeper understanding of the expectations and requirements for the Data Engineer role, as well as highlighting key areas of focus during your interview preparation.

The interview process for a Data Engineer position at Glatfelter is structured to assess both technical skills and cultural fit within the team. The process typically unfolds in the following stages:

1. Initial Contact

The process begins with an initial outreach from the HR team, usually via email or phone. This communication serves to confirm your application and schedule the first interview. During this stage, you may be asked to briefly discuss your background and interest in the role, as well as your understanding of the job description and responsibilities.

2. Manager Interview

The first formal interview is a short, approximately 15-minute conversation with the hiring manager. This interview focuses on your professional journey, key experiences, and how they relate to the Data Engineer role. The manager will likely assess your technical knowledge and gauge your enthusiasm for the position and the company.

3. Panel Interview

Following the initial interview, candidates typically participate in a panel interview with members of the data engineering team. This round is more in-depth and may cover a range of topics, including your technical skills, problem-solving abilities, and teamwork experiences. Expect to discuss specific projects you’ve worked on, the technologies you’ve used, and how you approach data-related challenges.

As you prepare for the interview, it’s essential to reflect on your past experiences and be ready to articulate how they align with the expectations of a Data Engineer at Glatfelter. Now, let’s delve into the types of questions you might encounter during this process.

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

Understand the Role and Responsibilities

Before your interview, take the time to thoroughly review the job description and understand the key responsibilities of a Data Engineer at Glatfelter. Familiarize yourself with the specific technologies and tools mentioned, as well as the data pipelines and architectures you may be working with. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the role.

Prepare for Different Interview Formats

Glatfelter's interview process may include both one-on-one and panel interviews. In the initial round, you might have a brief conversation with the hiring manager, so be prepared to succinctly summarize your background and relevant experiences. For the panel interview, anticipate a more in-depth discussion with multiple team members. Practice articulating your thoughts clearly and confidently, as well as engaging with different interviewers to showcase your collaborative spirit.

Highlight Relevant Experiences

When discussing your past experiences, focus on specific projects or challenges you've faced that relate to data engineering. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey not just what you did, but the impact of your work. This approach will help you stand out and provide concrete evidence of your capabilities.

Emphasize Problem-Solving Skills

Data engineering often involves troubleshooting and optimizing data processes. Be prepared to discuss how you've approached problem-solving in your previous roles. Share examples of how you identified issues, implemented solutions, and improved data workflows. This will demonstrate your analytical mindset and ability to think critically under pressure.

Showcase Your Technical Proficiency

Familiarize yourself with the technical skills and tools commonly used in data engineering, such as ETL processes, data warehousing, and programming languages like Python or SQL. Be ready to discuss your experience with these technologies and how you've applied them in real-world scenarios. If possible, bring examples of your work or projects to illustrate your technical expertise.

Align with Company Culture

Glatfelter values collaboration, innovation, and a commitment to quality. During your interview, reflect these values in your responses. Share examples of how you've worked effectively in teams, contributed to innovative solutions, and maintained high standards in your work. This alignment will help you resonate with the interviewers and demonstrate that you would be a good cultural fit.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers about the team dynamics, ongoing projects, and the company's vision for data engineering. This not only shows your interest in the role but also gives you valuable insights into whether Glatfelter is the right place for you. Tailor your questions based on the information you gather during the interview to make them more impactful.

By following these tips, you'll be well-prepared to make a strong impression during your interview at Glatfelter. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Glatfelter. The interview process will likely assess your technical skills, problem-solving abilities, and experience with data management and engineering practices. Be prepared to discuss your past experiences and how they relate to the responsibilities of a Data Engineer.

Technical Skills

1. What data engineering tools and technologies are you most familiar with, and how have you used them in your previous projects?

Glatfelter is interested in your hands-on experience with data engineering tools, as well as your ability to adapt to new technologies.

How to Answer

Discuss specific tools you have used, such as ETL frameworks, data warehousing solutions, or cloud platforms. Highlight how these tools contributed to the success of your projects.

Example

“I have extensive experience with Apache Spark for data processing and AWS Redshift for data warehousing. In my last project, I utilized Spark to process large datasets efficiently, which reduced our data processing time by 30%. This allowed our analytics team to access insights more quickly and make data-driven decisions.”

2. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is a fundamental part of data management.

How to Answer

Define the ETL process and explain its significance in ensuring data quality and accessibility for analysis.

Example

“The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. This process is vital because it ensures that data is clean, consistent, and readily available for analysis, which ultimately supports better business decisions.”

Data Modeling and Architecture

3. Describe a data model you designed in a previous role. What challenges did you face, and how did you overcome them?

This question assesses your practical experience in data modeling and your problem-solving skills.

How to Answer

Provide details about the data model, the challenges encountered, and the strategies you employed to address those challenges.

Example

“I designed a star schema for a retail analytics project, which involved integrating data from multiple sources. One challenge was ensuring data consistency across different systems. I implemented data validation rules during the ETL process, which helped maintain data integrity and improved the accuracy of our reports.”

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

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

How to Answer

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

Example

“I implement automated data quality checks at various stages of the data pipeline, such as validating data types and checking for duplicates. Additionally, I use logging and monitoring tools to track data flow and identify any anomalies, allowing for quick resolution of issues.”

Collaboration and Communication

5. How do you collaborate with data scientists and analysts to ensure that data meets their needs?

Collaboration is key in a data engineering role, and Glatfelter will want to see how you work with other teams.

How to Answer

Explain your approach to communication and collaboration, emphasizing the importance of understanding the needs of other stakeholders.

Example

“I regularly hold meetings with data scientists and analysts to understand their data requirements and gather feedback on the data I provide. This collaborative approach ensures that the data is not only accurate but also tailored to their analytical needs, which enhances the overall effectiveness of our data initiatives.”

6. Can you provide an example of a time when you had to explain a complex technical concept to a non-technical audience?

This question evaluates your communication skills and ability to convey technical information clearly.

How to Answer

Share a specific instance where you successfully communicated a complex idea, focusing on your approach to simplifying the information.

Example

“I once had to explain our data pipeline architecture to a group of marketing professionals. I used visual aids and analogies to break down the components of the pipeline, which helped them understand how data flows from collection to analysis. This not only clarified the process for them but also fostered better collaboration on data-driven marketing strategies.”