Our questions are designed to test your practical knowledge and your ability to apply engineering principles to real-world pharmaceutical data challenges.

Technical & Coding

• How do you handle data skewness in a Spark join?
• Explain the difference between rank(), dense_rank(), and row_number() in SQL.
• Write a Python script to parse a nested JSON file and flatten it into a tabular format.
• Describe the process of schema evolution in an AWS Glue Data Catalog.
• How would you implement incremental loading for a dataset that receives millions of updates daily?

Architectural & Scenario-Based

• Walk me through the most complex data pipeline you have ever built. What were the biggest challenges?
• If a production pipeline fails at 2 AM, what is your step-by-step process for identifying the root cause?
• How do you balance the need for fast data delivery with the requirement for strict data quality and compliance?
• Describe a time you had to choose between two different technologies for a project. What factors influenced your decision?

Behavioral & Leadership

• Tell me about a time you had a disagreement with a teammate or stakeholder. How did you resolve it?
• Describe a situation where you had to work with a technology you were unfamiliar with. How did you get up to speed?
• At Lilly, we value "Integrity, Excellence, and Respect for People." How have you demonstrated these values in your previous roles?
• Give an example of a project where you took the initiative to improve a process without being asked.

The visual timeline above illustrates the standard progression from initial contact to offer. Most candidates will complete the process within 3 to 5 weeks, depending on scheduling and the specific needs of the hiring team. Use this timeline to pace your preparation, ensuring you have deep-dived into your technical projects before reaching the onsite stages.

Deep Dive into Evaluation Areas

Big Data Processing & PySpark

As we deal with immense volumes of clinical and research data, mastery of PySpark is essential. We don't just look for basic syntax knowledge; we want to see that you understand how to optimize distributed computing jobs and manage resource allocation effectively.

Be ready to go over:

• Transformations and Actions – Deep understanding of lazy evaluation and the Spark execution plan.
• Performance Tuning – Strategies for handling data skew, partitioning, and caching.
• Window Functions – Practical application of complex analytical queries over partitioned data.
• Advanced concepts – Broadcast joins, UDF performance implications, and Spark UI debugging.

Example scenarios:

• "How would you optimize a PySpark job that is consistently failing due to Out-of-Memory (OOM) errors on a specific join?"
• "Explain the difference between a narrow and wide transformation and how each impacts stage boundaries."

Cloud Infrastructure (AWS)

Most of our modern data platforms are built on AWS. We evaluate your ability to leverage managed services to build "well-architected" data solutions that are secure, scalable, and cost-effective.

Be ready to go over:

• AWS Glue – Using Glue for ETL, cataloging, and schema evolution.
• Storage Strategy (S3) – Organizing data lakes, partitioning strategies, and lifecycle policies.
• Serverless Compute – Integrating AWS Lambda for event-driven data processing.
• Data Warehousing – Understanding the role of Redshift or Athena in the broader ecosystem.

Example scenarios:

• "Walk us through a serverless data pipeline you designed using AWS Glue and S3. How did you handle error logging and retries?"
• "When would you choose Athena over Redshift for querying data stored in S3?"

Data Modeling & SQL Optimization

The integrity of our research depends on well-structured data. You must demonstrate an ability to design schemas that support both high-speed ingestion and complex downstream analytics.

Be ready to go over:

• Schema Design – Dimensional modeling, Star schemas vs. Snowflake schemas.
• SQL Mastery – Complex joins, CTEs, and advanced windowing.
• Data Quality – Implementing validation checks and handling "dirty" data within the pipeline.

Example scenarios:

• "Design a data model for a clinical trial tracking system. How do you handle many-to-many relationships between patients and treatments?"
• "Rewrite a poorly performing SQL query that involves multiple nested subqueries and large table scans."

Key Responsibilities

As a Data Engineer at Eli Lilly and, your primary responsibility is to design, develop, and maintain the automated data pipelines that power our global operations. You will be tasked with ingesting data from a variety of internal and external sources, ensuring it is cleaned, transformed, and loaded into our data lakes and warehouses with 100% accuracy.

You will collaborate closely with Data Scientists to understand their modeling requirements and provide them with "feature-ready" datasets. This often involves complex feature engineering and the implementation of robust data validation frameworks to ensure that the insights derived from the data are medically sound.

Beyond pipeline development, you will also play a key role in operational excellence. This includes monitoring production pipelines, troubleshooting failures in real-time, and continuously looking for ways to improve the performance and reliability of our infrastructure. You will also participate in architectural reviews, contributing your expertise to help shape the long-term data strategy of the organization.

Role Requirements & Qualifications

We are looking for experienced engineers who can balance technical precision with a focus on business impact. Successful candidates typically demonstrate a strong background in software engineering principles applied to data problems.

• Technical Skills – Expert-level proficiency in Python and SQL. Extensive experience with PySpark and the AWS ecosystem (Glue, S3, Lambda, IAM).
• Experience Level – Typically 3+ years of experience for P3 roles, with 7+ years and demonstrated leadership for P5/Senior roles. Experience in a regulated industry (Pharma, Finance, Healthcare) is a significant advantage.
• Soft Skills – Excellent communication skills and the ability to explain technical trade-offs to stakeholders. A "team-first" mentality and a proactive approach to problem-solving.

Must-have skills:

• Hands-on experience building production-grade ETL pipelines.
• Deep understanding of distributed systems and cloud architecture.
• Strong proficiency in data modeling and relational database design.

Nice-to-have skills:

• Experience with Terraform or other Infrastructure-as-Code (IaC) tools.
• Familiarity with Airflow for orchestration.
• Knowledge of GXP compliance and data privacy regulations (GDPR/HIPAA).

Frequently Asked Questions

Q: How technical is the managerial interview? A: It is a hybrid. While the focus is on behavioral traits and leadership, our managers are technically savvy. Expect to discuss technical scenarios, production reliability, and how you align your engineering work with broader business goals.

Q: What is the most important thing to emphasize during the technical deep dive? A: Focus on the "why." Don't just list the tools you used; explain why they were the right choice for that specific problem, what alternatives you considered, and how you measured the success of the solution.

Q: How much does the specific technology stack matter? A: While we primarily use AWS and PySpark, we value strong engineering fundamentals. However, being "shocked" by a different stack in the interview is rare; we typically look for candidates whose experience aligns with our core tools to ensure a smooth transition.

Q: What is the culture like for engineers at Eli Lilly and? A: It is professional, mission-driven, and highly collaborative. People here genuinely love the company's mission. You will find a high level of respect for work-life balance, but a very high bar for the quality and accuracy of your work.

Other General Tips

• Master the STAR Method: For behavioral questions, ensure your answers follow the Situation, Task, Action, and Result format. Be specific about your individual contribution to the result.
• Clarify Ambiguity: If a technical scenario is vague, ask clarifying questions before you start designing. This shows you have a structured approach to problem-solving.
• Highlight Compliance: In the pharmaceutical industry, data security and compliance are paramount. Mentioning your experience with data governance or auditing will set you apart.
• Be Honest About Your Stack: If you haven't used a specific AWS service, admit it, but explain how your experience with a similar tool (e.g., Azure Data Factory vs. AWS Glue) allows you to learn quickly.

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Summary & Next Steps

A career as a Data Engineer at Eli Lilly and offers the rare opportunity to apply cutting-edge data engineering practices to problems that truly matter. From optimizing the delivery of medicines to uncovering insights in clinical data, your work will have a tangible impact on global health.

The interview process is rigorous because the stakes are high. By focusing your preparation on PySpark optimization, AWS architecture, and clear communication of your previous impact, you can demonstrate that you have the technical and professional maturity required to succeed here. Remember that we are looking for colleagues, not just coders—show us your passion for the mission and your ability to work as part of a high-performing team.

The salary range for this position reflects our commitment to attracting top-tier engineering talent. Compensation is determined based on a combination of your technical expertise, years of experience, and the specific level (P3-P5) for which you are being evaluated. Beyond base salary, Eli Lilly and offers a comprehensive benefits package designed to support your long-term career growth and personal well-being.

We encourage you to explore more detailed interview insights and community-reported questions on Dataford to further refine your preparation. We look forward to meeting you and seeing how your skills can help us continue to make life better for patients worldwide.