Illumina Data Engineer Interview Questions + Guide in 2025

Illumina is a pioneering company in the genomics industry, dedicated to improving human health through innovative sequencing and array-based solutions.

As a Data Engineer at Illumina, you will play a crucial role in building and maintaining the infrastructure necessary for managing large-scale genomic data. Key responsibilities include designing and implementing data pipelines, ensuring data integrity, and optimizing data storage solutions to support various genomic applications. You will collaborate closely with data scientists and researchers to facilitate data access and streamline workflows.

To excel in this role, strong programming skills in languages such as Python, Java, or Scala are essential, along with experience in cloud computing environments and proficiency in databases, both SQL and NoSQL. A solid understanding of data modeling and ETL processes is critical. Traits such as attention to detail, problem-solving abilities, and a collaborative mindset will help you thrive in Illumina's innovative and fast-paced environment.

This guide will equip you with insights into the expectations and challenges of the Data Engineer role at Illumina, enabling you to prepare effectively for your interview and demonstrate your alignment with the company’s mission and values.

The interview process for a Data Engineer position at Illumina is structured to evaluate both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the collaborative environment. The process typically includes the following stages:

1. Application and Initial Screening

Candidates begin by submitting their applications online, which may be supplemented by participation in recruitment events hosted by Illumina. Following this, an initial screening is conducted, often through a behavioral video interview. This stage focuses on understanding the candidate's background, motivations, and alignment with Illumina's values and culture.

2. Coding Challenge

After successfully passing the initial screening, candidates are required to complete a coding challenge. This challenge assesses the candidate's programming skills and problem-solving abilities, typically involving tasks relevant to data engineering, such as data manipulation, ETL processes, or algorithm design.

3. Technical Phone Interview

Candidates who perform well in the coding challenge will proceed to a technical phone interview. This interview is conducted by a member of the data engineering team and delves deeper into the candidate's technical expertise. Expect questions related to data structures, algorithms, database management, and specific technologies relevant to the role.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which is comprehensive and multifaceted. Candidates participate in various activities designed to assess teamwork, leadership, technical skills, and presentation abilities. This may include collaborative problem-solving exercises, technical assessments, and discussions that evaluate the candidate's approach to real-world data engineering challenges.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Illumina. The interview process will assess your technical skills, problem-solving abilities, and how well you can work within a team. Be prepared to demonstrate your knowledge of data structures, algorithms, and data processing techniques, as well as your understanding of the life sciences domain.

Technical Skills

1. What are SNPs and how do you find them?

Understanding Single Nucleotide Polymorphisms (SNPs) is crucial in the context of genomic data processing, especially at a company like Illumina.

How to Answer

Explain what SNPs are and discuss the methods used to identify them, such as sequencing technologies and bioinformatics tools.

Example

“SNPs, or Single Nucleotide Polymorphisms, are variations at a single position in a DNA sequence among individuals. They can be identified through high-throughput sequencing methods, followed by alignment and variant calling using bioinformatics tools like GATK or SAMtools.”

2. Describe your experience with ETL processes.

ETL (Extract, Transform, Load) processes are fundamental in data engineering, and your experience with them will be evaluated.

How to Answer

Discuss specific ETL tools you have used, the types of data you have worked with, and any challenges you faced during the process.

Example

“I have extensive experience with ETL processes using Apache NiFi and Talend. In my previous role, I developed a pipeline to extract genomic data from various sources, transform it to fit our data model, and load it into a data warehouse, ensuring data integrity and quality throughout the process.”

Data Structures and Algorithms

3. Can you explain the difference between a stack and a queue?

Understanding data structures is essential for any data engineering role, and this question tests your foundational knowledge.

How to Answer

Clearly define both data structures and provide examples of when you would use each.

Example

“A stack is a Last In First Out (LIFO) data structure, while a queue is a First In First Out (FIFO) structure. I would use a stack for scenarios like backtracking algorithms, while a queue is ideal for managing tasks in a scheduling system.”

4. How would you optimize a slow-running SQL query?

Performance optimization is a key skill for data engineers, and this question assesses your problem-solving abilities.

How to Answer

Discuss various strategies for query optimization, such as indexing, query rewriting, and analyzing execution plans.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I would consider adding indexes on frequently queried columns, rewriting the query to reduce complexity, and ensuring that I’m only selecting the necessary columns to minimize data retrieval time.”

Behavioral Questions

5. Describe a time when you had to work as part of a team to solve a complex problem.

Collaboration is vital in data engineering, and this question evaluates your teamwork skills.

How to Answer

Provide a specific example that highlights your role in the team, the problem you faced, and the outcome.

Example

“In a previous project, our team was tasked with integrating disparate data sources into a unified system. I took the initiative to facilitate communication between team members, ensuring everyone’s input was valued. This collaborative approach led to a successful integration that improved our data accessibility and reporting capabilities.”

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

Time management and prioritization are essential skills for a data engineer, especially in a fast-paced environment.

How to Answer

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

Example

“I prioritize tasks by assessing their urgency and impact on project goals. I use project management tools like Jira to track progress and deadlines, allowing me to allocate my time effectively across multiple projects while ensuring that critical tasks are completed on schedule.”