Eli Lilly And Company Data Engineer Interview Questions + Guide in 2025

Eli Lilly And Company is a global healthcare leader dedicated to discovering and delivering life-changing medicines to those in need.

The Data Engineer role at Eli Lilly involves designing, developing, and maintaining efficient data pipelines and architectures that support the company's mission in pharmaceuticals and healthcare innovation. Key responsibilities include ensuring data integrity and quality, integrating various data sources, and optimizing data storage solutions. A successful Data Engineer at Eli Lilly should possess strong analytical skills, a deep understanding of data management principles, and proficiency in programming languages and tools relevant to data engineering, such as SQL, Python, and cloud technologies. Additionally, the ability to collaborate effectively with cross-functional teams and communicate complex technical concepts to non-technical stakeholders is essential. This role is integral to supporting Eli Lilly's commitment to leveraging data for improved patient outcomes and operational excellence.

Preparing for your interview using this guide will help you articulate your skills and experiences effectively, aligning them with Eli Lilly's values and mission, thus giving you a competitive edge in the hiring process.

The interview process for a Data Engineer position at Eli Lilly is structured and thorough, reflecting the company's commitment to finding the right fit for their innovative team. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, which usually takes place via a phone call with a recruiter or HR representative. This conversation lasts about 30 minutes and focuses on your background, experiences, and motivations for wanting to work at Eli Lilly. Expect to discuss your resume in detail, including your technical skills and any relevant projects. The recruiter will also assess your cultural fit within the company.

2. Technical Assessment

Following the initial screening, candidates often undergo a technical assessment. This may include a coding challenge or a technical interview that tests your knowledge of data engineering principles, programming languages (such as Python or SQL), and data architecture. The assessment can be conducted through an online platform or during a video call, where you may be asked to solve problems in real-time. Be prepared for questions related to data structures, algorithms, and specific technologies relevant to the role.

3. Onsite or Virtual Interviews

Candidates who pass the technical assessment are typically invited for a series of onsite or virtual interviews. This stage usually consists of multiple rounds, often including one-on-one interviews with team members and managers. These interviews will delve deeper into your technical expertise, problem-solving abilities, and past experiences. Expect to encounter behavioral questions that require you to use the STAR (Situation, Task, Action, Result) method to articulate your responses. You may also be asked to present a project or discuss your approach to data integration and management.

4. Final Interview

The final interview often involves discussions with higher-level management or cross-functional teams. This stage may focus on your understanding of Eli Lilly's mission and how your skills align with their goals. You might also discuss your long-term career aspirations and how you envision contributing to the company's success. This is a good opportunity to ask questions about the team dynamics, company culture, and future projects.

5. Offer and Negotiation

If you successfully navigate the interview process, you will receive a job offer. This stage may involve discussions about salary, benefits, and other employment terms. Eli Lilly is known for its competitive compensation packages, so be prepared to negotiate based on your experience and the industry standards.

As you prepare for your interviews, consider the types of questions that may arise during the process.

Here are some tips to help you excel in your interview for the Data Engineer role at Eli Lilly and Company.

Embrace the Company Culture

Eli Lilly values a collaborative and supportive work environment. During your interview, demonstrate your ability to work well in teams and your enthusiasm for contributing to a culture that prioritizes caring and discovery. Be prepared to share examples of how you've successfully collaborated with others in past roles, as this aligns with the company's emphasis on teamwork and community.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions during your interviews. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences, particularly those that highlight your problem-solving skills, ability to handle conflict, and adaptability in challenging situations. Given the friendly nature of the interviewers, approach these questions with confidence and authenticity.

Showcase Your Technical Skills

Technical proficiency is crucial for a Data Engineer role. Brush up on your knowledge of data architecture, SQL, Python, and cloud technologies, particularly AWS. Be ready to discuss specific projects where you applied these skills, and consider preparing for coding challenges that may arise during the interview process. Familiarize yourself with common data structures and algorithms, as well as data integration and management practices.

Understand the Role's Requirements

Familiarize yourself with the specific responsibilities and expectations outlined in the job description. Be prepared to discuss how your experience aligns with the need for designing scalable data solutions, implementing data governance, and collaborating with cross-functional teams. Highlight any relevant experience in the pharmaceutical or healthcare sectors, as this can set you apart from other candidates.

Engage with Your Interviewers

The interview process at Eli Lilly often includes informal interactions, such as lunch with employees. Use these opportunities to ask insightful questions about the company culture, team dynamics, and ongoing projects. This not only shows your interest in the role but also helps you gauge if the company is the right fit for you.

Be Open and Honest

Interviewers appreciate candidates who are genuine and transparent about their experiences and aspirations. If you encounter a question you’re unsure about, it’s okay to admit it and discuss how you would approach finding a solution. This demonstrates your willingness to learn and grow, which is a valued trait at Eli Lilly.

Follow Up with Gratitude

After your interview, send a thank-you email to express your appreciation for the opportunity to interview and reiterate your interest in the role. This small gesture can leave a positive impression and reinforce your enthusiasm for joining the Eli Lilly team.

By preparing thoroughly and embodying the values of Eli Lilly, you can position yourself as a strong candidate 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 Eli Lilly. The interview process will likely focus on a combination of technical skills, problem-solving abilities, and behavioral competencies. Candidates should be prepared to discuss their experience with data architecture, integration, and management, as well as their ability to work collaboratively in a team environment.

Technical Skills

1. Can you explain your experience with data architecture and integration patterns?

This question aims to assess your understanding of data architecture principles and your practical experience in implementing them.

How to Answer

Discuss specific projects where you designed or implemented data architecture solutions. Highlight the technologies you used and the challenges you faced.

Example

“In my previous role, I designed a data architecture for a healthcare analytics platform that integrated data from various sources, including EHRs and lab systems. I utilized AWS services for data storage and processing, ensuring scalability and compliance with healthcare regulations.”

2. Describe a time when you had to optimize a data pipeline. What steps did you take?

This question evaluates your problem-solving skills and your ability to enhance data processing efficiency.

How to Answer

Outline the specific issues you encountered, the analysis you performed, and the optimizations you implemented.

Example

“I noticed that our data pipeline was experiencing latency due to inefficient ETL processes. I analyzed the bottlenecks and implemented parallel processing, which reduced the data load time by 40% and improved overall system performance.”

3. What tools and technologies do you prefer for data management and why?

This question assesses your familiarity with industry-standard tools and your rationale for choosing them.

How to Answer

Mention specific tools you have used, such as SQL databases, ETL tools, or cloud platforms, and explain why they are effective for your work.

Example

“I prefer using Apache Spark for data processing due to its speed and ability to handle large datasets. For data storage, I often use Amazon Redshift because of its scalability and integration with other AWS services.”

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

This question focuses on your approach to maintaining high data standards.

How to Answer

Discuss the methods you use for data validation, cleansing, and monitoring.

Example

“I implement data validation checks at various stages of the ETL process to catch errors early. Additionally, I use automated monitoring tools to track data quality metrics and alert the team to any anomalies.”

5. Can you describe your experience with cloud data solutions?

This question evaluates your knowledge and experience with cloud technologies relevant to data engineering.

How to Answer

Share specific examples of cloud platforms you have worked with and the types of solutions you implemented.

Example

“I have extensive experience with AWS, particularly in setting up data lakes using S3 and processing data with AWS Glue. I also utilized Redshift for analytics, which allowed us to scale our data processing capabilities significantly.”

Behavioral Questions

1. Tell me about a time you faced a conflict in a team setting. How did you handle it?

This question assesses your interpersonal skills and ability to navigate workplace challenges.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response.

Example

“In a previous project, there was a disagreement about the data model design. I facilitated a meeting where each team member could present their perspective. By encouraging open communication, we reached a consensus that combined the best elements of both designs, leading to a more robust solution.”

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 and any tools or methods you use.

Example

“I use a combination of project management tools like Jira and a priority matrix to assess the urgency and impact of tasks. This helps me focus on high-impact activities while ensuring that deadlines are met across all projects.”

3. Describe a situation where you had to learn a new technology quickly. How did you approach it?

This question assesses your adaptability and willingness to learn.

How to Answer

Share a specific instance where you successfully learned a new technology and how you applied it.

Example

“When our team decided to migrate to a new data visualization tool, I dedicated time to online courses and hands-on practice. Within a few weeks, I was able to lead a training session for my colleagues, which helped us transition smoothly.”

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

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

How to Answer

Reflect on what aspects of data engineering excite you and how they align with your career goals.

Example

“I am motivated by the potential of data to drive decision-making and improve outcomes, especially in healthcare. The opportunity to work on projects that can directly impact patient care is what drives my passion for data engineering.”

5. Why do you want to work for Eli Lilly?

This question assesses your interest in the company and its mission.

How to Answer

Discuss your alignment with the company’s values and how you see yourself contributing to its goals.

Example

“I admire Eli Lilly’s commitment to innovation in healthcare and its focus on improving patient lives. I believe my skills in data engineering can contribute to the development of data-driven solutions that enhance drug discovery and patient outcomes.”