Cardlytics, Inc. Data Engineer Interview Questions + Guide in 2025

Cardlytics, Inc. is an industry-leading purchase intelligence and incentives platform that transforms how businesses connect with consumers through digital banking channels.

As a Data Engineer at Cardlytics, you will play a pivotal role in developing and enhancing the core data platform that underpins the company's various product offerings. Your key responsibilities will include designing and building scalable data architectures, creating applications and features that facilitate data services for cross-functional teams, and ensuring the integrity and efficiency of data pipelines. To excel in this role, you should be proficient in programming languages such as Scala, Java, or Python, and have extensive experience in building large-scale distributed systems. Your analytical prowess and problem-solving skills will be essential as you navigate complex data challenges and collaborate with teams across the organization.

Success in this role requires not only technical expertise but also a deep commitment to Cardlytics' values of customer-first thinking, accountability, and empowerment. This guide will prepare you for your interview by highlighting key focus areas and essential skills, ensuring you stand out as a strong candidate for the Data Engineer position.

The interview process for a Data Engineer at Cardlytics is designed to assess both technical skills and cultural fit within the organization. It typically consists of several distinct stages:

1. Initial Screening

The process begins with an initial screening conducted by a recruiter. This is usually a brief phone call where the recruiter will discuss your background, experience, and interest in the role. They will also provide insights into the company culture and expectations for the position. This stage is crucial for determining if you align with Cardlytics' values and if you possess the foundational skills required for the role.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may include a coding challenge or a HackerRank test that evaluates your proficiency in SQL and programming languages such as Python, Scala, or Java. The assessment is designed to gauge your ability to solve problems and build scalable data systems, which are essential skills for a Data Engineer at Cardlytics.

3. Behavioral Interview

Candidates who perform well in the technical assessment will typically move on to a behavioral interview. This interview focuses on your past experiences, teamwork, and problem-solving abilities. Expect questions that explore your previous projects, how you handle challenges, and your approach to collaboration with cross-functional teams. This stage is particularly important as it helps assess your fit within the company culture and your alignment with Cardlytics' core values.

4. Final Interview

The final interview often involves meeting with senior team members or management. This stage may include more in-depth technical discussions, as well as a review of your previous work and how it relates to the responsibilities of the Data Engineer role. You may also be asked to present a project or solution you have worked on, demonstrating your analytical skills and ability to communicate complex ideas effectively.

Throughout the interview process, candidates should be prepared to discuss their technical expertise, particularly in building large-scale distributed systems and data platforms, as well as their ability to write clean and maintainable code.

Now that you have an understanding of the interview process, let's delve into the specific questions that candidates have encountered during their interviews at Cardlytics.

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

Prepare for Behavioral Questions

Cardlytics places a strong emphasis on behavioral questions during the interview process. Be ready to discuss your past projects and experiences in detail. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you highlight your problem-solving skills and how you’ve collaborated with cross-functional teams. Given the feedback from previous candidates, it’s important to convey your ability to work under pressure and manage competing priorities effectively.

Showcase Your Technical Skills

As a Data Engineer, proficiency in SQL and programming languages like Python is crucial. Be prepared to demonstrate your technical skills through coding assessments or technical interviews. Brush up on your SQL knowledge, focusing on complex queries, data manipulation, and performance optimization. Additionally, familiarize yourself with distributed systems and data pipeline architectures, as these are key components of the role.

Understand the Company Culture

Cardlytics values a customer and partner-first approach, along with integrity and accountability. During your interview, reflect these values in your responses. Show that you are not only technically proficient but also aligned with the company’s mission to make commerce more rewarding. Discuss how you can contribute to a culture of empowerment and growth, and be prepared to share examples of how you’ve demonstrated these values in your previous roles.

Be Ready for a Fast-Paced Environment

Feedback indicates that the interview process can be chaotic and quick. This suggests that the work environment may also be fast-paced. Prepare to discuss how you handle rapid changes and tight deadlines. Highlight your ability to prioritize tasks and maintain quality under pressure, as well as your experience in managing multiple projects simultaneously.

Communicate Clearly and Confidently

Effective communication is essential at Cardlytics, especially when collaborating with various teams. Practice articulating your thoughts clearly and concisely. Be prepared to explain complex technical concepts in a way that non-technical stakeholders can understand. This will demonstrate your ability to bridge the gap between technical and non-technical teams, a valuable skill in any data engineering role.

Follow Up Professionally

Given the feedback about the lack of communication from the company post-interview, it’s important to follow up professionally after your interview. Send a thank-you email expressing your appreciation for the opportunity to interview and reiterating your enthusiasm for the role. This not only shows your professionalism but also keeps you on their radar.

By focusing on these areas, you can position yourself as a strong candidate for the Data Engineer role at Cardlytics. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Cardlytics. The interview process will likely focus on your technical skills, problem-solving abilities, and your experience with data infrastructure and software development. Be prepared to discuss your past projects, your approach to building scalable systems, and your ability to collaborate with cross-functional teams.

Technical Skills

1. Can you describe your experience with building large-scale distributed systems?

This question aims to assess your technical expertise and experience in designing systems that can handle large volumes of data.

How to Answer

Discuss specific projects where you designed or contributed to distributed systems, highlighting the technologies used and the challenges faced.

Example

“In my previous role, I was responsible for designing a distributed data processing system using Apache Spark. This system handled millions of transactions daily, and I implemented optimizations that improved processing speed by 30%. I also collaborated with the data science team to ensure the system met their analytical needs.”

2. What programming languages are you proficient in, and how have you applied them in your projects?

This question evaluates your coding skills and familiarity with relevant programming languages.

How to Answer

Mention the languages you are proficient in, and provide examples of how you used them in your work, particularly in data engineering contexts.

Example

“I am proficient in Python and Java. In my last project, I used Python to develop ETL processes that ingested data from various sources into our data warehouse. I also utilized Java for building microservices that processed and served data to our analytics team.”

3. How do you ensure the quality and maintainability of your code?

This question assesses your coding practices and commitment to software quality.

How to Answer

Discuss your approach to writing clean code, conducting code reviews, and implementing testing strategies.

Example

“I follow best practices such as writing modular code and adhering to naming conventions. I also participate in regular code reviews with my team to ensure consistency and quality. Additionally, I implement unit tests to catch issues early in the development process.”

4. Can you explain your experience with data pipeline orchestration tools like Airflow or Kafka?

This question focuses on your familiarity with tools that manage data workflows.

How to Answer

Provide details about your experience with these tools, including specific use cases and the benefits they brought to your projects.

Example

“I have extensive experience using Apache Airflow for orchestrating data pipelines. I designed workflows that automated data ingestion and processing tasks, which reduced manual intervention and improved reliability. I also used Kafka for real-time data streaming, enabling our analytics team to access data as it was generated.”

5. Describe a challenging data problem you faced and how you solved it.

This question evaluates your problem-solving skills and ability to handle complex data issues.

How to Answer

Share a specific example of a data challenge, the steps you took to address it, and the outcome.

Example

“In one project, we faced issues with data consistency due to multiple sources feeding into our system. I implemented a data validation layer that checked for discrepancies and flagged them for review. This solution improved our data accuracy and allowed us to trust the insights generated from our analytics.”

Behavioral Questions

1. What was your favorite project at your previous job, and why?

This question helps interviewers understand your interests and motivations.

How to Answer

Choose a project that you are passionate about, and explain what made it enjoyable and impactful.

Example

“My favorite project was developing a data visualization tool for our marketing team. I enjoyed collaborating with cross-functional teams to understand their needs and translating that into a user-friendly interface. The tool significantly improved their ability to analyze campaign performance, which was rewarding to see.”

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

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use.

Example

“I prioritize tasks based on their impact and deadlines. I use project management tools like Jira to track progress and ensure that I’m focusing on high-priority items. Regular check-ins with my team also help me adjust priorities as needed.”

3. Describe a time when you had to collaborate with a difficult team member. How did you handle it?

This question evaluates your interpersonal skills and ability to work in a team.

How to Answer

Share a specific example, focusing on how you navigated the situation and maintained a productive working relationship.

Example

“I once worked with a team member who was resistant to feedback. I approached the situation by having an open conversation to understand their perspective. By actively listening and finding common ground, we were able to collaborate more effectively and ultimately improve our project outcomes.”

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

This question assesses your commitment to continuous learning and professional development.

How to Answer

Mention specific resources, communities, or practices you engage in to stay informed.

Example

“I regularly read industry blogs, participate in webinars, and attend conferences related to data engineering. I’m also part of online communities where professionals share insights and best practices, which helps me stay current with emerging technologies.”

5. Can you give an example of how you’ve contributed to a team’s success?

This question focuses on your teamwork and collaboration skills.

How to Answer

Provide a specific example of your contributions and the positive impact they had on the team.

Example

“In my last role, I took the initiative to mentor junior engineers on best practices for data modeling. By sharing my knowledge and providing guidance, I helped them improve their skills, which ultimately led to more efficient project delivery and a stronger team dynamic.”