Novartis Data Engineer Interview Questions + Guide in 2025

Novartis is a global healthcare company focused on innovative medicines and solutions to improve and extend people's lives.

The Data Engineer role at Novartis encompasses the design, development, and management of data pipelines and architectures that enable the analysis and utilization of vast amounts of healthcare data. Key responsibilities include developing robust data models, ensuring data quality, and collaborating with cross-functional teams to support data-driven decision-making. Required skills include proficiency in programming languages such as Python or Java, experience with SQL and NoSQL databases, and a deep understanding of data warehousing concepts. A great fit for this role also involves possessing a strong analytical mindset, exceptional problem-solving skills, and the ability to communicate technical concepts to non-technical stakeholders.

This guide will help you prepare for a job interview by highlighting key areas of focus specific to Novartis and the Data Engineer role, ensuring you present your skills and experiences effectively.

The interview process for a Data Engineer position at Novartis 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 usually involves a screening call with a recruiter. This conversation lasts about 10-30 minutes and focuses on your background, experience, and motivation for applying to Novartis. The recruiter will also provide an overview of the role and the company culture, ensuring that you have a clear understanding of what to expect.

2. Technical Assessment

Following the initial screening, candidates often undergo a technical assessment. This may include a coding challenge or a take-home project that allows you to demonstrate your data engineering skills at your own pace. The assessment is designed to evaluate your proficiency in relevant programming languages, data manipulation, and problem-solving abilities.

3. Behavioral Interviews

After successfully completing the technical assessment, candidates typically participate in one or more behavioral interviews. These interviews are conducted by hiring managers or team members and focus on your past experiences, teamwork, and how you handle challenges. Expect questions that explore your approach to collaboration, conflict resolution, and adaptability in a fast-paced environment.

4. Panel Interview

In some cases, candidates may be invited to a panel interview, which includes multiple interviewers from different departments. This format allows the team to assess your fit across various functions and gauge how well you align with Novartis' values and mission. Questions may cover both technical and situational scenarios, requiring you to think critically and articulate your thought process.

5. Final Interview and Offer

The final stage often involves a discussion with senior management or directors. This interview may delve deeper into your technical expertise and strategic thinking, as well as your long-term career aspirations. If successful, you will receive an offer call, where compensation and benefits will be discussed.

Throughout the process, communication is key, and candidates are encouraged to ask questions to ensure a mutual fit.

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

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

Understand the Company Culture

Novartis places a strong emphasis on collaboration, transparency, and a supportive work environment. Familiarize yourself with their core values and how they align with your own. During the interview, demonstrate your understanding of the company culture by discussing how you thrive in collaborative settings and how you value open communication. This will show that you are not only a technical fit but also a cultural fit for the organization.

Prepare for Technical and Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Brush up on your data engineering skills, including SQL, Python, and data modeling. Be ready to discuss your past projects in detail, focusing on your problem-solving approach and the impact of your work. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions, as this will help you convey your experiences clearly and effectively.

Showcase Your Problem-Solving Skills

Given the nature of the role, you may be presented with case studies or hypothetical scenarios to assess your analytical thinking and problem-solving abilities. Practice articulating your thought process when tackling complex data challenges. Be prepared to explain how you would approach a problem, the tools you would use, and how you would measure success. This will demonstrate your ability to think critically and apply your technical knowledge in real-world situations.

Communicate Clearly and Confidently

Effective communication is key in any interview, especially in a role that requires collaboration across teams. Practice explaining technical concepts in a way that is accessible to non-technical stakeholders. During the interview, maintain a confident demeanor, make eye contact, and engage with your interviewers. This will help you build rapport and convey your enthusiasm for the role.

Be Ready for a Panel Interview

Many candidates have reported experiencing panel interviews at Novartis, where multiple interviewers assess your fit for the role. Prepare for this format by practicing with a friend or mentor who can simulate the experience. Be ready to address questions from different perspectives, as each interviewer may focus on various aspects of your skills and experiences.

Follow Up with Thoughtful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Use this time to inquire about the team dynamics, ongoing projects, and how success is measured in the role. This not only shows your interest in the position but also helps you gauge if the company aligns with your career goals.

Stay Patient and Persistent

The interview process at Novartis can be lengthy, with multiple rounds and potential delays. Stay patient and maintain a positive attitude throughout the process. If you experience delays in communication, don’t hesitate to follow up politely. This demonstrates your continued interest in the position and your proactive nature.

By following these tips, you will be well-prepared to navigate the interview process at Novartis and showcase your qualifications for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Novartis. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the organization. Be prepared to discuss your experience with data management, engineering principles, and your approach to collaboration in a team environment.

Technical Skills

1. 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 fundamental to data integration and management.

How to Answer

Discuss the steps involved in ETL and emphasize its role 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 as it ensures that data is clean, consistent, and readily available for analysis, which is essential for informed decision-making in a pharmaceutical context.”

2. What are some common data storage solutions you have worked with?

Familiarity with various data storage solutions is essential for a Data Engineer, as it impacts how data is managed and accessed.

How to Answer

Mention specific technologies you have used, such as SQL databases, NoSQL databases, or cloud storage solutions, and explain your experience with them.

Example

“I have experience working with both SQL databases like PostgreSQL and NoSQL solutions like MongoDB. For instance, I used PostgreSQL for structured data storage in a project that required complex queries, while MongoDB was ideal for handling unstructured data in another project.”

3. Describe a challenging data pipeline you built. What were the key considerations?

This question assesses your problem-solving skills and your ability to design efficient data pipelines.

How to Answer

Outline the challenges you faced, the solutions you implemented, and the impact of your work on the project.

Example

“I built a data pipeline that integrated data from multiple sources, including APIs and databases. The key considerations were ensuring data integrity and optimizing for performance. I implemented data validation checks and used parallel processing to improve efficiency, which reduced the data processing time by 30%.”

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

Data quality is critical in data engineering, especially in a regulated industry like pharmaceuticals.

How to Answer

Discuss the methods you use to maintain data quality, such as validation, testing, and monitoring.

Example

“I ensure data quality by implementing validation rules during the ETL process, conducting regular audits, and using automated testing frameworks. Additionally, I monitor data pipelines for anomalies and set up alerts for any discrepancies.”

5. What programming languages and tools do you prefer for data engineering tasks?

Your proficiency in programming languages and tools is essential for a Data Engineer role.

How to Answer

Mention the languages and tools you are most comfortable with and provide examples of how you have used them in your work.

Example

“I primarily use Python for data manipulation and ETL tasks, leveraging libraries like Pandas and NumPy. Additionally, I am proficient in SQL for database management and have experience with Apache Spark for big data processing.”

Behavioral Questions

1. Describe a time when you had to work with a difficult team member. How did you handle it?

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

How to Answer

Provide a specific example, focusing on your approach to resolving conflicts and fostering teamwork.

Example

“In a previous project, I worked with a team member who was resistant to feedback. I scheduled a one-on-one meeting to discuss our differing perspectives and actively listened to their concerns. By finding common ground and establishing clear communication, we were able to collaborate effectively and complete the project successfully.”

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

This question evaluates 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.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools like Trello to track progress and set reminders for critical milestones. This approach helps me stay organized and ensures that I meet project deadlines without compromising quality.”

3. Can you give an example of a time you had to adapt to a significant change in a project?

Adaptability is crucial in a fast-paced environment, and this question assesses your flexibility.

How to Answer

Share a specific instance where you successfully adapted to change, highlighting your problem-solving skills.

Example

“During a project, we had to switch from a monolithic architecture to a microservices approach due to scalability issues. I quickly researched best practices and collaborated with the team to redesign the data architecture. This change ultimately improved our system’s performance and scalability.”

4. What motivates you to work in the data engineering field?

Understanding your motivation helps interviewers gauge your passion for the role.

How to Answer

Share your enthusiasm for data engineering and how it aligns with your career goals.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. The ability to work on projects that can impact patient outcomes in the pharmaceutical industry excites me, as I believe data-driven decisions can lead to significant advancements in healthcare.”

5. How do you stay updated with the latest trends and technologies in data engineering?

This question assesses your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to stay informed, such as online courses, webinars, or industry publications.

Example

“I stay updated by following industry blogs, participating in online forums, and attending webinars. I also take online courses on platforms like Coursera to learn about emerging technologies and best practices in data engineering.”