Discover Data Engineer Interview Questions + Guide in 2025

Discover is a leading digital banking and payments company dedicated to helping customers achieve a brighter financial future.

As a Data Engineer at Discover, you will be responsible for designing, developing, testing, and maintaining complex data solutions that drive the company’s innovative services. This role requires you to troubleshoot and optimize data integration solutions, ensuring high-quality outcomes while mentoring peers and contributing to the agile ceremonies. You will leverage your strong technical aptitude to utilize various tools for data profiling, security, and governance while designing advanced SQL queries and participating in code reviews. A key aspect of your role will involve effective communication and collaboration with cross-functional teams to ensure the successful delivery of data-driven products.

To excel in this role at Discover, you should possess a bachelor's degree in Computer Science or a related field along with at least three years of experience in data engineering and administration. Familiarity with ETL/ELT tools, cloud databases, and programming languages like Python or Java will be beneficial. Additionally, a proactive mindset towards risk management and a commitment to the company's values of collaboration and continuous improvement will make you a great fit for the team.

This guide will equip you with insights into the expectations and responsibilities of the Data Engineer role at Discover, helping you prepare effectively for your interview and increase your chances of success.

The interview process for a Data Engineer position at Discover is structured to assess both technical skills and cultural fit within the organization. It typically consists of several rounds, each designed to evaluate different aspects of your qualifications and experience.

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter or a senior manager. This conversation lasts about 30 minutes and focuses on your background, experience, and understanding of the role. The recruiter will also provide insights into Discover's culture and values, ensuring that you align with their collaborative and growth-oriented environment.

2. Technical Interviews

Following the initial screening, candidates typically undergo multiple technical interviews, often spanning three consecutive days. Each interview lasts approximately 45 minutes and is conducted by different senior managers or technical leads. These interviews delve into your technical expertise, particularly in data engineering practices, SQL proficiency, and experience with data integration tools. Expect questions that relate to your resume and past job responsibilities, as well as problem-solving scenarios that test your ability to design and troubleshoot complex data solutions.

3. Behavioral Assessment

In addition to technical evaluations, candidates will also face behavioral interviews. These sessions assess your soft skills, teamwork, and ability to influence and mentor peers. Interviewers will be interested in your experiences working in agile environments and how you handle risk and customer-impacting issues. Be prepared to discuss specific examples that demonstrate your collaborative spirit and commitment to quality.

4. Final Interview

The final stage may involve a wrap-up interview with higher management or a panel. This session is an opportunity for you to ask questions about the team dynamics, project expectations, and the company's strategic direction. It also serves as a chance for the interviewers to gauge your enthusiasm for the role and your alignment with Discover's mission and values.

As you prepare for your interviews, consider the specific questions that may arise during the process, focusing on both technical and behavioral aspects.

Here are some tips to help you excel in your interview for the Data Engineer role at Discover.

Understand the Interview Structure

Be prepared for a multi-round interview process, which may include an initial discussion with a senior manager followed by several technical rounds. Each round typically lasts around 45 minutes and focuses on your technical skills and experiences as outlined in your resume. Familiarize yourself with the types of data solutions you have worked on, as interviewers will likely ask you to elaborate on your past projects and responsibilities.

Prepare for Technical Questions

While the job description emphasizes ETL experience, be aware that interview questions may not always align directly with the job description. For instance, you might encounter questions that are more aligned with database administration. Brush up on your SQL skills, particularly on advanced queries and execution plans, as these are common topics. Additionally, be ready to discuss data integration solutions, data transformations, and the tools you have used in your previous roles.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with hypothetical scenarios or problems related to data engineering. Approach these questions methodically: clarify the problem, outline your thought process, and explain how you would arrive at a solution. Demonstrating your critical thinking and problem-solving abilities will resonate well with the interviewers.

Emphasize Collaboration and Communication

Discover values a collaborative culture, so be prepared to discuss how you have worked effectively within teams. Highlight experiences where you influenced peers or contributed to team goals. Be ready to share examples of how you have communicated complex technical concepts to non-technical stakeholders, as effective communication is crucial in cross-functional teams.

Align with Company Culture

Familiarize yourself with Discover's core behaviors: "We Play to Win," "We Get Better Every Day," and "We Succeed Together." Reflect on how your personal values and work ethic align with these principles. Be prepared to discuss how you embody these behaviors in your work and how you can contribute to a culture of continuous improvement and teamwork.

Prepare for Behavioral Questions

Expect behavioral questions that assess your past experiences and how they relate to the role. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This will help you provide clear and concise answers that demonstrate your qualifications and fit for the role.

Stay Updated on Industry Trends

Being knowledgeable about the latest trends in data engineering, cloud technologies, and data governance will set you apart. Research current best practices and emerging technologies relevant to the role, and be prepared to discuss how you can apply this knowledge at Discover.

Practice, Practice, Practice

Finally, practice your technical skills and interview responses with a friend or mentor. Mock interviews can help you gain confidence and refine your answers. Additionally, consider reviewing common data engineering interview questions and practicing your responses to ensure you are well-prepared.

By following these tips, you will be well-equipped to make a strong impression during your interview for the Data Engineer role at Discover. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Discover. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data integration and management. Be prepared to discuss your past projects, the tools you've used, and how you approach data challenges.

Technical Skills

1. Can you explain the process of ETL and how you have implemented it in your previous projects?

Understanding ETL (Extract, Transform, Load) is crucial for a Data Engineer role, as it is a fundamental process in data integration.

How to Answer

Discuss your experience with ETL tools and frameworks, the specific challenges you faced, and how you overcame them. Highlight any specific technologies you used, such as Informatica or Apache NiFi.

Example

“In my previous role, I implemented an ETL process using Apache NiFi to extract data from various sources, transform it using Python scripts, and load it into a Snowflake data warehouse. One challenge was ensuring data quality during the transformation phase, which I addressed by implementing validation checks at each step.”

2. Describe your experience with SQL and how you optimize queries for performance.

SQL proficiency is essential for data manipulation and retrieval in this role.

How to Answer

Provide examples of complex queries you’ve written and the techniques you used to optimize them, such as indexing or query restructuring.

Example

“I frequently write complex SQL queries to extract insights from large datasets. To optimize performance, I use indexing on frequently queried columns and analyze execution plans to identify bottlenecks. For instance, I reduced query execution time by 30% by rewriting a subquery into a join.”

3. What tools and technologies do you use for data integration and why?

This question assesses your familiarity with the tools relevant to the role.

How to Answer

Mention specific tools you have experience with, such as Apache Kafka, Talend, or AWS Glue, and explain why you prefer them based on their features or your project requirements.

Example

“I primarily use Apache Kafka for real-time data streaming due to its high throughput and scalability. In a recent project, I integrated Kafka with Spark Streaming to process data in real-time, which significantly improved our data processing capabilities.”

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

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

How to Answer

Discuss the methods you use to validate data, such as automated testing, data profiling, and monitoring.

Example

“I implement data quality checks at various stages of the data pipeline. For instance, I use data profiling tools to assess incoming data for anomalies and set up alerts for any discrepancies. Additionally, I conduct regular audits to ensure data integrity.”

5. Can you explain a complex data transformation you have implemented?

This question allows you to showcase your problem-solving skills and technical expertise.

How to Answer

Describe a specific transformation, the challenges you faced, and the impact it had on the project.

Example

“In a project to consolidate customer data from multiple sources, I designed a complex transformation that involved deduplication and standardization of data formats. This required extensive use of Python and SQL, and the result was a unified dataset that improved our analytics capabilities by providing a single source of truth.”

Data Architecture

1. What is your experience with data modeling, and how do you approach it?

Data modeling is essential for structuring data effectively.

How to Answer

Discuss your experience with different data modeling techniques and tools, and how you ensure that the model meets business requirements.

Example

“I have experience with both star and snowflake schemas for data warehousing. In my last project, I used a star schema to optimize query performance for our BI tools, ensuring that the model was flexible enough to accommodate future data sources.”

2. How do you handle schema changes in a data pipeline?

Schema changes can disrupt data flows, so it's important to have a strategy in place.

How to Answer

Explain your approach to managing schema changes, including versioning and backward compatibility.

Example

“When faced with schema changes, I implement a versioning strategy that allows for backward compatibility. I also use tools like Apache Avro for schema evolution, which helps maintain data integrity while accommodating changes.”

3. Describe a time when you had to troubleshoot a data pipeline issue. What was the problem and how did you resolve it?

This question assesses your problem-solving skills and ability to work under pressure.

How to Answer

Provide a specific example of a troubleshooting scenario, detailing the steps you took to identify and fix the issue.

Example

“Once, I encountered a data pipeline failure due to a misconfigured data source. I quickly reviewed the logs to identify the error, corrected the configuration, and implemented additional logging to catch similar issues in the future. This proactive approach reduced downtime significantly.”

4. What strategies do you use for data governance and compliance?

Data governance is crucial for ensuring data security and compliance with regulations.

How to Answer

Discuss your understanding of data governance principles and any frameworks or tools you have used.

Example

“I follow best practices for data governance by implementing role-based access controls and regularly auditing data access logs. Additionally, I ensure compliance with regulations like GDPR by anonymizing sensitive data in our pipelines.”

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

This question gauges your commitment to continuous learning.

How to Answer

Mention any resources you use, such as online courses, blogs, or conferences, to keep your skills current.

Example

“I regularly follow industry blogs, participate in webinars, and attend conferences like the Data Engineering Summit. I also take online courses on platforms like Coursera to learn about emerging technologies and best practices in data engineering.”