Discover Financial Services Data Engineer Interview Questions + Guide in 2025

Discover Financial Services is a leading digital banking and payments company committed to helping millions of people achieve a brighter financial future.

The Data Engineer role at Discover is pivotal in designing, developing, and maintaining complex data solutions within a collaborative and agile environment. Key responsibilities include executing data integration solutions, recognizing data patterns, and actively participating in design decisions. This role requires a strong foundation in data engineering practices and tools, including SQL and ETL processes, as well as familiarity with cloud-based solutions and data integration methodologies. Ideal candidates possess a Bachelor’s degree in Computer Science or a related field, along with practical experience in data platform administration and engineering. A commitment to continuous improvement and teamwork aligns with Discover’s core values of collaboration and growth.

This guide will equip you with insights to prepare effectively for your interview, helping you to showcase your technical skills and align your experience with Discover’s mission and values.

The interview process for a Data Engineer position at Discover Financial Services is structured to assess both technical skills and cultural fit within the organization. The process typically consists of several key stages:

1. Initial Phone Screen

The first step is a phone interview with a recruiter, lasting about 30 minutes. During this conversation, the recruiter will review your resume and ask general questions about your background, experiences, and motivations for applying to Discover. This is also an opportunity for you to learn more about the company culture and the specifics of the Data Engineer role.

2. Technical Screening

Following the initial screen, candidates will undergo a technical interview with a hiring manager or a senior engineer. This session is usually conducted via video call and focuses on assessing your technical knowledge and problem-solving abilities. Expect questions related to Java, Spring, and Spring Boot, as well as inquiries about microservices and data integration concepts. Familiarity with SQL and data profiling tools may also be evaluated during this stage.

3. Onsite Interview

The onsite interview consists of multiple rounds, typically involving 3 to 5 one-on-one interviews with various team members. These interviews will cover a range of topics, including data engineering practices, data integration solutions, and cloud technologies. You may be asked to demonstrate your understanding of data modeling, ETL/ELT processes, and data security. Additionally, behavioral questions will be included to assess your teamwork and communication skills, as collaboration is a core value at Discover.

4. Final Assessment

In some cases, there may be a final assessment or presentation where candidates are asked to showcase a project or a solution they have worked on. This is an opportunity to demonstrate your technical expertise and your ability to communicate complex ideas effectively.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during these stages.

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

Master the Technical Fundamentals

As a Data Engineer at Discover Financial Services, you will be expected to have a strong grasp of SQL, Java, and Spring Boot. Make sure to review key concepts and practice coding problems related to these technologies. Given the emphasis on Java and Spring Boot in the technical screen, familiarize yourself with their core functionalities, best practices, and common pitfalls. Prepare to discuss how you have used these technologies in past projects, focusing on specific challenges you faced and how you overcame them.

Embrace the Agile Mindset

Discover values collaboration and continuous improvement, which are central to their agile culture. Be prepared to discuss your experience with agile methodologies, including how you have contributed to team ceremonies and how you handle feedback. Highlight instances where you have worked closely with cross-functional teams to deliver data solutions, emphasizing your role in fostering a collaborative environment.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your problem-solving abilities. Practice articulating your thought process when faced with data integration challenges or design trade-offs. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly outline the context and your specific contributions to the solution.

Communicate Clearly and Effectively

Effective communication is crucial in a collaborative environment like Discover. Be prepared to explain complex technical concepts in a way that is accessible to non-technical stakeholders. Practice discussing your past projects and the impact they had on the business, focusing on how you communicated your findings and recommendations to various audiences.

Prepare for Behavioral Questions

Expect behavioral questions that align with Discover's core values: "We Play to Win," "We Get Better Every Day," and "We Succeed Together." Reflect on your past experiences and be ready to share examples that demonstrate your alignment with these values. Consider how you have contributed to team success, learned from failures, and driven improvements in your work.

Understand the Company Culture

Familiarize yourself with Discover's mission and values. They emphasize a collaborative culture where individual uniqueness is celebrated. Be prepared to discuss how your personal values align with the company's culture and how you can contribute to fostering an inclusive environment.

Ask Insightful Questions

At the end of the interview, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Inquire about the team dynamics, ongoing projects, and how success is measured in the Data Engineering team. This not only shows your enthusiasm but also helps you assess if Discover is the right fit for you.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Discover Financial Services. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Discover Financial Services. The interview will likely focus on your technical skills, particularly in data integration, SQL, and software development practices. Be prepared to discuss your experience with data solutions, cloud technologies, and your approach to problem-solving in a collaborative environment.

Technical Skills

1. Can you explain the differences between ETL and ELT processes?

Understanding the distinctions between these two data processing methods is crucial for a Data Engineer role.

How to Answer

Discuss the definitions of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), highlighting their use cases and advantages in different scenarios.

Example

"ETL is a process where data is extracted from various sources, transformed into a suitable format, and then loaded into a data warehouse. This is ideal for structured data. In contrast, ELT allows data to be loaded into the warehouse first and then transformed, which is more efficient for handling large volumes of unstructured data, especially in cloud environments."

2. What are some common data integration tools you have used?

This question assesses your familiarity with industry-standard tools.

How to Answer

Mention specific tools you have experience with, such as Informatica, Talend, or AWS Glue, and describe your role in using them.

Example

"I have worked extensively with Talend for data integration projects, where I designed workflows to automate data extraction and loading processes. Additionally, I have experience with AWS Glue for serverless data integration, which streamlined our ETL processes significantly."

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

Data quality is critical in data engineering, and interviewers want to know your approach.

How to Answer

Discuss methods you use to validate and clean data, such as data profiling, validation rules, and automated testing.

Example

"I implement data quality checks at various stages of the ETL process. This includes profiling data to identify anomalies, applying validation rules during data transformation, and conducting regular audits to ensure data integrity. Automated testing scripts also help catch issues early in the development cycle."

4. Describe your experience with SQL. What advanced functions have you used?

SQL proficiency is essential for a Data Engineer, and this question gauges your depth of knowledge.

How to Answer

Talk about your experience with SQL, including complex queries, joins, and any advanced functions like window functions or stored procedures.

Example

"I have a strong command of SQL, having used it to write complex queries involving multiple joins and subqueries. I frequently utilize window functions for analytical tasks, such as calculating running totals and ranking data within partitions, which has been invaluable for reporting purposes."

5. Can you explain the concept of data modeling?

Data modeling is a fundamental aspect of data engineering, and understanding it is crucial.

How to Answer

Define data modeling and discuss its importance in structuring data for efficient access and analysis.

Example

"Data modeling is the process of creating a visual representation of data structures and relationships. It is essential for ensuring that data is organized in a way that supports efficient querying and reporting. I have experience in both dimensional and relational modeling, which helps in designing databases that meet business requirements."

Programming and Development

1. What programming languages are you proficient in, and how have you used them in data engineering?

This question assesses your programming skills relevant to the role.

How to Answer

Mention the programming languages you are familiar with, such as Python or Java, and provide examples of how you have applied them in data projects.

Example

"I am proficient in Python and Java. I have used Python for data manipulation and analysis, leveraging libraries like Pandas and NumPy. In Java, I have developed microservices for data processing, which improved the scalability of our data pipelines."

2. How do you approach debugging a data pipeline?

Debugging is a critical skill for a Data Engineer, and interviewers want to know your methodology.

How to Answer

Describe your systematic approach to identifying and resolving issues in data pipelines.

Example

"When debugging a data pipeline, I start by reviewing logs to identify where the failure occurred. I then isolate the problematic component, whether it's a data source, transformation logic, or loading process. I also use test data to replicate the issue and validate the fix before deploying changes."

3. Can you discuss your experience with version control systems?

Version control is essential for collaborative development, and this question assesses your familiarity with tools like Git.

How to Answer

Talk about your experience using version control systems, including how you manage code changes and collaborate with team members.

Example

"I regularly use Git for version control in my projects. I follow best practices by creating feature branches for new developments and conducting code reviews before merging changes into the main branch. This approach helps maintain code quality and facilitates collaboration within the team."

4. What is your experience with cloud platforms, and how have you utilized them in your projects?

Cloud technologies are increasingly important in data engineering, and this question gauges your experience.

How to Answer

Discuss specific cloud platforms you have worked with, such as AWS, GCP, or Azure, and how you have leveraged their services.

Example

"I have extensive experience with AWS, particularly with services like S3 for data storage and Redshift for data warehousing. I have also utilized AWS Lambda for serverless data processing, which has allowed us to build scalable and cost-effective data solutions."

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

This question assesses your commitment to continuous learning in a rapidly evolving field.

How to Answer

Share your strategies for keeping your skills current, such as attending conferences, taking online courses, or participating in professional communities.

Example

"I stay updated by following industry blogs, participating in webinars, and attending conferences related to data engineering. I also take online courses to learn about new tools and technologies, ensuring that I can apply the latest best practices in my work."