GSK Data Engineer Interview Questions + Guide in 2025

GSK is a global biopharma company dedicated to uniting science, technology, and talent to advance healthcare and combat disease.

As a Data Engineer at GSK, your primary responsibility will be to design, develop, and maintain automated data services and pipelines that facilitate the efficient handling and analysis of data. You will work closely with scientists and engineers to ensure that they have access to the right data and insights, enabling them to drive medical discovery. Your role will require a strong foundation in software engineering principles, as well as proficiency in modern data engineering tools such as Python, Spark, and Kafka.

Key responsibilities include building modular code and services, creating data processing pipelines, ensuring the quality and compliance of data processes, and providing support for existing tools and services. The ideal candidate will possess a bachelor’s degree in a related field and have experience in writing data processing pipelines and working with databases and SQL. GSK values continuous learning and collaboration, so being receptive to feedback and eager to improve your technical skills is essential.

This guide will help you prepare for your interview by providing insights into the role and expectations, equipping you with the knowledge to effectively showcase your skills and alignment with GSK’s mission.

The interview process for a Data Engineer role at GSK is structured and designed to assess both technical and behavioral competencies. It typically consists of several stages, ensuring a comprehensive evaluation of candidates.

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter. This is a brief phone call where the recruiter will discuss your resume, verify your qualifications, and gauge your interest in the role. Expect questions about your background, motivation for applying, and general fit for GSK's culture.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a coding test, often conducted through platforms like HackerRank, where you will be evaluated on your ability to write data processing pipelines and solve algorithmic problems. The focus will be on your proficiency in relevant programming languages such as Python and SQL, as well as your understanding of data structures and algorithms.

3. Technical Interview

Candidates who pass the technical assessment will move on to a technical interview. This round typically involves a panel of interviewers, including senior data engineers and team leads. Expect in-depth discussions about your previous projects, experience with data engineering tools (like Spark, Kafka, and cloud platforms), and your approach to building scalable data solutions. You may also be asked to explain your thought process in solving specific technical challenges.

4. Behavioral Interview

In addition to technical skills, GSK places a strong emphasis on cultural fit and behavioral competencies. The behavioral interview will focus on your past experiences and how they align with GSK's values. Be prepared to answer situational questions using the STAR (Situation, Task, Action, Result) method, highlighting your teamwork, problem-solving abilities, and how you handle conflicts or challenges in a professional setting.

5. Final Interview

The final stage often involves a discussion with higher management or cross-functional team members. This interview may cover your long-term career aspirations, your understanding of GSK's mission, and how you can contribute to the team. It’s also an opportunity for you to ask questions about the company culture and the specific projects you would be involved in.

Throughout the process, candidates are encouraged to demonstrate their passion for data engineering and their eagerness to learn and grow within the role.

Next, let's delve into the specific interview questions that candidates have encountered during this process.

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

Embrace the Company Culture

GSK values a collaborative and inclusive environment where employees feel inspired and encouraged. During your interview, demonstrate your alignment with these values by sharing experiences that highlight your teamwork, adaptability, and commitment to fostering a positive workplace. Be prepared to discuss how you can contribute to a culture of innovation and support, as this will resonate well with the interviewers.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions that assess your fit with GSK's values. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced challenges, collaborated with stakeholders, or made a quantifiable impact. This will not only showcase your problem-solving skills but also your ability to work effectively within a team.

Highlight Technical Proficiency

As a Data Engineer, your technical skills are crucial. Be ready to discuss your experience with data processing pipelines, SQL, and relevant programming languages like Python and Spark. Prepare to explain your approach to building scalable data solutions and your familiarity with tools such as Kafka and cloud environments. Providing specific examples of projects where you applied these skills will strengthen your candidacy.

Show Enthusiasm for Learning

GSK seeks candidates who are eager to learn and grow. During the interview, express your passion for data engineering and your commitment to continuous improvement. Discuss any recent courses, certifications, or personal projects that demonstrate your proactive approach to skill development. This will signal to the interviewers that you are not only qualified but also motivated to contribute to GSK's mission.

Be Ready for Technical Assessments

You may encounter technical assessments or coding challenges during the interview process. Brush up on your coding skills and familiarize yourself with common data engineering problems. Practice coding exercises that involve SQL queries, data structures, and algorithms, as these are likely to be part of the evaluation. Being well-prepared will help you approach these challenges with confidence.

Communicate Clearly and Confidently

Throughout the interview, maintain clear and confident communication. When discussing your experiences, articulate your thought process and the rationale behind your decisions. This will help interviewers understand your problem-solving approach and technical expertise. Additionally, don’t hesitate to ask clarifying questions if you need more information about a topic or task presented during the interview.

Follow Up Thoughtfully

After the interview, send a thoughtful follow-up email to express your gratitude for the opportunity to interview. Reiterate your enthusiasm for the role and briefly mention a key point from the discussion that resonated with you. This not only shows your professionalism but also reinforces your interest in the position.

By following these tips, you will be well-prepared to make a strong impression during your interview with GSK. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at GSK. The interview process will likely focus on your technical skills, experience with data processing, and your ability to work collaboratively within a team. Be prepared to discuss your past projects, the tools you’ve used, and how you approach problem-solving in data engineering.

Technical Skills

1. Can you describe your experience with data processing pipelines?

This question aims to assess your hands-on experience in building and maintaining data pipelines.

How to Answer

Discuss specific projects where you designed or implemented data pipelines, the tools you used, and the challenges you faced.

Example

“In my previous role, I built a data processing pipeline using Apache Spark to handle large datasets. I implemented ETL processes that transformed raw data into a structured format, which improved data accessibility for our analytics team.”

2. What programming languages are you proficient in, and how have you used them in your projects?

This question evaluates your technical proficiency and practical application of programming languages in data engineering.

How to Answer

Mention the languages you are most comfortable with, provide examples of how you’ve used them in your work, and highlight any relevant libraries or frameworks.

Example

“I am proficient in Python and SQL. In my last project, I used Python for data manipulation and analysis, leveraging libraries like Pandas and NumPy. I also wrote complex SQL queries to extract and aggregate data from our relational databases.”

3. How do you ensure the quality and consistency of data in your pipelines?

This question focuses on your understanding of data quality and governance practices.

How to Answer

Explain the methods you use to validate data, monitor data quality, and ensure compliance with data governance standards.

Example

“I implement automated testing for my data pipelines to catch errors early. Additionally, I use logging and monitoring tools to track data quality metrics and set up alerts for any anomalies.”

4. Can you explain the differences between a data lake and a data warehouse?

This question tests your understanding of data storage solutions and their use cases.

How to Answer

Provide a clear distinction between the two concepts, including their architecture, purpose, and types of data they handle.

Example

“A data lake is designed to store vast amounts of raw data in its native format, making it suitable for big data analytics. In contrast, a data warehouse stores structured data that has been processed for analysis, making it ideal for business intelligence and reporting.”

5. What experience do you have with cloud platforms and data engineering tools?

This question assesses your familiarity with modern data engineering environments.

How to Answer

Discuss any cloud platforms you’ve worked with, the tools you’ve used, and how they contributed to your data engineering tasks.

Example

“I have experience working with AWS, specifically using services like S3 for data storage and Redshift for data warehousing. I also utilized Apache Kafka for real-time data streaming in a cloud-based architecture.”

Behavioral Questions

1. Describe a time when you faced a significant challenge in a project. How did you overcome it?

This question evaluates your problem-solving skills and resilience.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response, focusing on the challenge and your approach to resolving it.

Example

“In a previous project, we encountered performance issues with our data pipeline. I analyzed the bottlenecks and optimized the code, which reduced processing time by 30%. This not only improved efficiency but also enhanced team morale.”

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

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use to manage your workload effectively.

Example

“I prioritize tasks based on project deadlines and impact. I use project management tools like Jira to track progress and ensure that I’m focusing on high-priority tasks that align with team goals.”

3. Can you give an example of how you collaborated with a cross-functional team?

This question evaluates your teamwork and communication skills.

How to Answer

Share a specific instance where you worked with other teams, highlighting your role and the outcome of the collaboration.

Example

“I collaborated with data scientists and product managers to develop a new analytics feature. I facilitated regular meetings to ensure alignment and shared updates on data availability, which led to a successful launch of the feature on time.”

4. What motivates you to work in data engineering?

This question aims to understand your passion and commitment to the field.

How to Answer

Reflect on what excites you about data engineering and how it aligns with your career goals.

Example

“I am motivated by the potential of data to drive decision-making and innovation. The challenge of transforming raw data into actionable insights is what excites me, and I am eager to contribute to projects that have a meaningful impact on healthcare.”

5. How do you handle feedback and criticism?

This question assesses your ability to accept and learn from feedback.

How to Answer

Discuss your approach to receiving feedback and how you use it to improve your work.

Example

“I view feedback as an opportunity for growth. When I receive constructive criticism, I take the time to reflect on it and implement changes in my work. This mindset has helped me continuously improve my skills and deliver better results.”