Novartis Data Engineer interview questions & guide 2026

The following questions reflect the patterns observed in recent interview cycles. While the specific focus can shift depending on the team’s current project priorities, these topics represent the core competencies Novartis evaluates for Data Engineering talent.

Technical Modeling and Architecture

These questions assess your ability to design robust data structures that can handle real-world complexity and scale.

• How do you model maps data?
• Can you describe your approach to designing a scalable data pipeline for high-volume research data?

Preparation at Novartis requires a blend of deep technical proficiency and the ability to articulate your thought process clearly. You should be prepared to go beyond just "writing code" and demonstrate how your engineering choices support the broader business goals of a pharmaceutical leader.

Technical Proficiency – This covers your core engineering skills, including data modeling, pipeline development, and database architecture. Interviewers look for evidence that you can build systems that are not only functional but also performant and maintainable. You should be ready to discuss the "why" behind every tool or framework you choose.

Analytical Problem-Solving – You will be evaluated on your ability to decompose ambiguous, complex problems into manageable technical requirements. Focus on your methodology for identifying edge cases, managing data dependencies, and ensuring the reliability of your outputs.

Communication of Technical Logic – Because you will collaborate with cross-functional teams, your ability to explain complex technical concepts to non-technical stakeholders is vital. Practice articulating your design decisions clearly, focusing on the impact your solutions have on data integrity and project timelines.

The interview process at Novartis is structured to be thorough, typically spanning 3 to 4 rounds of technical evaluation. The process generally begins with an initial video interview to establish your background and technical fit. Following this, you will likely complete a technical assessment—a hands-on project designed to gauge your practical engineering skills—before moving into final rounds where you will defend your work and discuss your methodology with senior team members.

The culture of the interview process is professional and focused. You should expect an environment that values rigor; interviewers are interested in your technical depth and your ability to handle the specific constraints of the problem space. The process is designed to mimic the collaborative nature of the actual role, so expect to be challenged on your assumptions and your approach to collaborative problem-solving.

The timeline above highlights the progression from initial screening to deeper technical validation. Candidates should view the assessment stage as the centerpiece of their preparation, as subsequent rounds often pivot to deep-dive discussions on the work you submitted. Manage your time to ensure you have enough buffer to refine your assessment project before the final technical defense.

Data Modeling and Architecture

This area is critical because the data landscape at Novartis is highly heterogeneous. You must demonstrate an ability to create flexible, resilient models that can handle disparate data sources. Strong performance involves showing that you understand the lifecycle of data, from ingestion to consumption.

Be ready to go over:

• Schema design – Balancing performance with flexibility for evolving research needs.
• Data normalization vs. denormalization – Understanding when to prioritize query speed versus data consistency.

As a Data Engineer, your primary objective is to build and maintain the infrastructure that supports data-driven decision-making. You will spend a significant portion of your time developing and optimizing ETL/ELT pipelines, ensuring that data is transformed accurately and delivered on time to downstream users.

Collaboration is a daily requirement. You will work closely with Data Scientists and Research Analysts to understand their data requirements, translating their needs into performant technical solutions. You are also responsible for monitoring the health of these pipelines, troubleshooting bottlenecks, and implementing best practices for data governance and security, which are paramount in a regulated industry like pharmaceuticals.

To be competitive, you should possess a strong background in distributed systems and data processing frameworks. While specific tools may vary by team, the underlying principles of robust software engineering remain constant.

• Must-have skills – Advanced proficiency in SQL and at least one programming language (e.g., Python or Scala), significant experience with cloud-based data platforms, and a deep understanding of pipeline orchestration tools.
• Nice-to-have skills – Familiarity with pharmaceutical data standards, experience working in GxP-regulated environments, and knowledge of containerization technologies like Docker or Kubernetes.
• Experience – Candidates are expected to have a proven track record of delivering end-to-end data engineering projects, with a focus on scalability and automation.

Q: How difficult is the interview process? The process is considered challenging, largely due to the focus on the technical assessment. Expect to be questioned deeply on your implementation choices.

Q: How much time should I spend preparing? Given the assessment component, allocate significant time to practice your coding and system design skills. Ensure you are comfortable explaining your work in detail.

Q: What differentiates successful candidates? Successful candidates are those who not only code well but also demonstrate a clear understanding of the business impact of their engineering work. Being able to justify your technical trade-offs is essential.

Q: Does Novartis offer remote work? Expectations can vary by location and team. It is best to confirm specific hybrid or remote policies during your initial recruiter screen.

• Articulate your trade-offs: Whenever you make a technical choice, be ready to explain why you chose it over alternatives.
• Focus on data quality: In the pharmaceutical sector, data accuracy is non-negotiable; always mention how you ensure the integrity of your pipelines.
• Master your assessment: Do not treat your assessment as a "take-home" task to be forgotten; it will be the primary subject of your final technical interviews.
• Stay curious about the domain: Showing an interest in how your engineering work helps patients can set you apart from candidates who focus only on the code.

The Data Engineer role at Novartis is a high-impact position that offers the chance to apply advanced engineering skills to meaningful, global challenges. By focusing your preparation on robust data modeling, defending your architectural decisions, and demonstrating a clear understanding of data reliability, you will position yourself as a top-tier candidate.

Remember that you can explore additional interview insights, practice questions, and preparation resources on Dataford to further refine your strategy. With thorough preparation and a focus on the core evaluation areas outlined here, you have every opportunity to succeed.

The module above provides insights into the compensation landscape for this role. Use these figures as a benchmark to understand market expectations, keeping in mind that total compensation may include various components such as base salary, performance bonuses, and other benefits depending on your seniority and location.