PubMatic Data Engineer Interview Questions + Guide in 2025

PubMatic is an independent technology company focused on maximizing customer value through innovative solutions in digital advertising.

The Data Engineer role at PubMatic is a pivotal position responsible for designing, building, and maintaining robust big data platforms that process terabytes of data for in-depth analytics. Key responsibilities include developing big data pipelines using technologies such as Hadoop, Spark, and Kafka, as well as implementing architectural solutions that enhance data processing efficiency. A successful data engineer at PubMatic will be self-motivated, possess strong coding skills (particularly in Java), and have a solid understanding of software engineering best practices. The ideal candidate will thrive in a fast-paced, collaborative environment, actively engaging in Agile/Scrum processes and demonstrating excellent communication skills.

This guide will help you prepare effectively for your interview by providing insights into the key skills and responsibilities associated with the Data Engineer role at PubMatic, increasing your confidence and readiness to impress the interviewers.

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

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place over a phone call with a recruiter. This conversation lasts about 30 minutes and focuses on your background, skills, and motivations for applying to PubMatic. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you have a clear understanding of what to expect.

2. Technical Interviews

Following the initial screening, candidates typically undergo two technical interviews. These interviews are designed to evaluate your proficiency in key areas such as SQL, Java, and object-oriented programming principles. You may be asked to solve coding problems in real-time, which could include writing algorithms or developing data structures. Additionally, expect questions related to big data technologies like Hadoop and Spark, as well as your experience with data pipeline development and management.

3. System Design Interview

In this round, you will be tasked with designing a system or architecture that addresses a specific problem relevant to PubMatic's operations. This interview assesses your ability to think critically about system scalability, fault tolerance, and data processing efficiency. You may be asked to discuss your design choices and how they align with best practices in software engineering.

4. Behavioral Interview

The final round is a behavioral interview, where the focus shifts to your interpersonal skills and how you align with PubMatic's values. You will be asked about your past experiences working in teams, handling challenges, and contributing to projects. This is an opportunity to showcase your communication skills and your ability to collaborate effectively in a fast-paced environment.

Throughout the interview process, be prepared to discuss your previous work experiences, particularly those that demonstrate your problem-solving abilities and technical expertise.

Next, let's delve into the specific interview questions that candidates have encountered during the process.

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

Understand the Technical Landscape

Familiarize yourself with the big data technologies that are central to the role, such as Hadoop, Spark, Kafka, HBase, and Hive. Be prepared to discuss your experience with these technologies and how you have applied them in previous projects. Additionally, brush up on your Java skills, as coding proficiency is crucial for this position. Practice coding challenges that involve data structures and algorithms, as these are fundamental to the role.

Prepare for Technical Questions

Expect technical questions that assess your understanding of software engineering best practices, including coding standards, code reviews, and the software development life cycle. Be ready to explain your approach to building scalable and fault-tolerant systems, as well as how you would handle data processing pipelines. You may also be asked to write code on the spot, so practice coding exercises in a timed environment to simulate the interview experience.

Showcase Problem-Solving Skills

PubMatic values self-motivated problem solvers. Be prepared to discuss specific challenges you have faced in your previous roles and how you overcame them. Use the STAR (Situation, Task, Action, Result) method to structure your responses, highlighting your analytical thinking and ability to learn new technologies quickly.

Emphasize Collaboration and Communication

Given the collaborative nature of the role, demonstrate your interpersonal skills and ability to work in cross-functional teams. Be ready to discuss how you have effectively communicated technical concepts to non-technical stakeholders. Highlight any experience you have with Agile/Scrum methodologies, as this will show your familiarity with the team dynamics at PubMatic.

Align with Company Culture

PubMatic promotes a vibrant and transparent work environment. Research the company’s values and culture, and think about how your personal values align with theirs. Be prepared to discuss why you are excited about the opportunity to work at PubMatic and how you can contribute to their mission of maximizing customer value in digital advertising.

Ask Insightful Questions

Prepare thoughtful questions to ask your interviewers that demonstrate your interest in the role and the company. Inquire about the team’s current projects, challenges they face, and how success is measured in the role. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.

By following these tips, you will be well-prepared to make a strong impression during your interview at PubMatic. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at PubMatic. The interview will likely focus on your technical skills, particularly in big data technologies, software engineering practices, and problem-solving abilities. Be prepared to demonstrate your knowledge of data processing, pipeline development, and system design.

Technical Skills

1. Can you explain the differences between Hadoop and Spark?

Understanding the strengths and weaknesses of these two big data frameworks is crucial for a Data Engineer role.

How to Answer

Discuss the core functionalities of both frameworks, highlighting their use cases, performance differences, and when to use one over the other.

Example

"Hadoop is primarily a batch processing framework that uses the MapReduce paradigm, which can be slower for certain tasks. In contrast, Spark is designed for in-memory processing, making it significantly faster for iterative algorithms and real-time data processing. I would choose Spark for applications requiring low latency and real-time analytics, while Hadoop is suitable for large-scale batch processing tasks."

2. Describe your experience with building data pipelines. What tools did you use?

This question assesses your hands-on experience with data pipeline development.

How to Answer

Provide specific examples of projects where you built data pipelines, mentioning the tools and technologies you used.

Example

"I developed a data pipeline using Apache Kafka for real-time data ingestion and Apache Spark for processing. The pipeline collected data from various sources, transformed it, and loaded it into a data warehouse for analytics. This setup allowed us to process millions of records per day with minimal latency."

3. How do you ensure data quality 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 strategies you implement to validate and clean data, as well as any tools you use for monitoring data quality.

Example

"I implement data validation checks at various stages of the pipeline, such as schema validation and anomaly detection. I also use tools like Apache Airflow to monitor the pipeline and alert the team if any data quality issues arise, ensuring that we can address them promptly."

4. What is your experience with SQL and how do you use it in your projects?

SQL is a fundamental skill for data engineers, and this question evaluates your proficiency.

How to Answer

Share your experience with SQL, including the types of queries you write and how you optimize them for performance.

Example

"I have extensive experience writing complex SQL queries for data extraction and transformation. I often use window functions and joins to analyze large datasets efficiently. Additionally, I focus on query optimization techniques, such as indexing and partitioning, to improve performance."

5. Can you explain the concept of ETL and how you have implemented it in your work?

Understanding ETL (Extract, Transform, Load) processes is essential for a Data Engineer.

How to Answer

Describe your experience with ETL processes, including the tools you used and any challenges you faced.

Example

"I have implemented ETL processes using Apache NiFi to extract data from various sources, transform it using Apache Spark, and load it into a data warehouse. One challenge I faced was handling schema changes in source data, which I addressed by implementing a flexible transformation layer that could adapt to changes without breaking the pipeline."

Software Engineering Practices

1. How do you approach code reviews?

This question assesses your understanding of best practices in software development.

How to Answer

Discuss your philosophy on code reviews, including what you look for and how you provide feedback.

Example

"I believe code reviews are essential for maintaining code quality and fostering collaboration. I focus on readability, adherence to coding standards, and potential performance issues. I also encourage open discussions during reviews to share knowledge and improve team practices."

2. Describe a time when you had to troubleshoot a production issue. What steps did you take?

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

How to Answer

Provide a specific example of a production issue you encountered, detailing the steps you took to diagnose and resolve it.

Example

"When a data pipeline failed in production, I first checked the logs to identify the error. I then traced the issue back to a recent change in the source data format. I quickly implemented a temporary fix to bypass the issue while I worked on a more permanent solution, which involved updating the transformation logic to handle the new format."

3. What software development methodologies are you familiar with?

Understanding software development methodologies is important for collaboration in an agile environment.

How to Answer

Mention the methodologies you have experience with and how they have influenced your work.

Example

"I am familiar with Agile and Scrum methodologies, which I have used in several projects. I appreciate the iterative approach and the emphasis on collaboration, which helps teams adapt to changing requirements and deliver value more quickly."

4. How do you document your code and processes?

Documentation is key in software engineering, and this question assesses your practices.

How to Answer

Discuss the tools and methods you use for documentation, as well as the importance of maintaining clear documentation.

Example

"I use Confluence for documenting processes and decisions, and I ensure that my code is well-commented to explain complex logic. I also create README files for projects to provide an overview and usage instructions, making it easier for others to understand and contribute."

5. Can you give an example of a project where you had to learn a new technology quickly?

This question evaluates your adaptability and willingness to learn.

How to Answer

Share a specific instance where you had to learn a new technology and how you approached it.

Example

"When tasked with implementing a new data processing framework, I had to learn Apache Flink quickly. I dedicated time to studying the documentation and completed several tutorials. Within a week, I was able to build a prototype that successfully processed streaming data, demonstrating my ability to adapt and learn new technologies efficiently."