Kayak Data Engineer Interview Questions + Guide in 2025

Kayak, a leading travel search engine under Booking Holdings, facilitates billions of queries to help users find their ideal travel arrangements, from flights to vacation packages.

As a Data Engineer at Kayak, you will be pivotal in shaping the data landscape that supports the company's mission of enhancing travel experiences. This role encompasses a variety of key responsibilities, including the creation of dashboards and data visualizations to provide insights into product performance, building and maintaining data pipelines using technologies such as Python, SQL, Airflow, and Docker, and collaborating closely with cross-functional teams to understand business needs and propose data-driven solutions. Ideal candidates will possess strong SQL skills, experience in data engineering or analytics, and the ability to write clean code, with a preference for those familiar with ETL processes and data modeling.

Emphasizing teamwork, innovation, and a commitment to data democratization, this role aligns with Kayak's values of collaboration and customer-centricity. Preparing for your interview using this guide will help you articulate your technical expertise, demonstrate how your skills align with Kayak’s goals, and ultimately give you a competitive edge in the hiring process.

The interview process for a Data Engineer role at Kayak is structured and thorough, designed to assess both technical skills and cultural fit within the organization.

1. Initial Screening

The process typically begins with a phone screening conducted by an HR recruiter. This initial conversation lasts about 30 minutes and focuses on your background, motivations for applying, and general fit for the company culture. Expect to answer behavioral questions that gauge your alignment with Kayak's values and work environment.

2. Technical Assessment

Following the initial screening, candidates are often required to complete a technical assessment. This may involve a take-home coding assignment or an online programming quiz that tests your proficiency in SQL, Python, and data engineering concepts. The assignment is designed to evaluate your ability to write clean, efficient code and solve data-related problems.

3. Technical Interviews

Candidates who successfully complete the technical assessment will move on to a series of technical interviews. Typically, there are two to four rounds of interviews, which may include one-on-one sessions with team leads, engineers, or the hiring manager. These interviews will cover a range of topics, including data modeling, ETL processes, and SQL query optimization. Be prepared for both technical questions and practical coding exercises, as well as discussions about your previous projects and experiences.

4. Behavioral Interviews

In addition to technical assessments, candidates will also participate in behavioral interviews. These interviews focus on your past experiences, problem-solving abilities, and how you handle various work situations. Interviewers will likely ask about your teamwork, project management skills, and how you approach challenges in a collaborative environment.

5. Final Interview

The final stage of the interview process may involve a more in-depth discussion with higher-level management or cross-functional stakeholders. This round is often less technical and more focused on understanding your working style, how you would fit into the team, and your approach to collaboration across departments.

Throughout the process, candidates can expect a friendly and accommodating atmosphere, with interviewers keen to assess not only technical skills but also cultural fit within the Kayak team.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Understand the Interview Process

The interview process at KAYAK typically involves multiple stages, including an initial HR screening, a technical assessment, and interviews with various team members. Familiarize yourself with this structure and prepare accordingly. Be ready for both technical and behavioral questions, as the company places a strong emphasis on cultural fit. Knowing what to expect can help you feel more at ease and confident during your interviews.

Showcase Your Technical Skills

As a Data Engineer, proficiency in SQL and Python is crucial. Brush up on your SQL skills, focusing on complex queries, data modeling, and performance optimization. Practice writing clean and efficient code in Python, as you may be asked to demonstrate your coding abilities during the interview. Additionally, familiarize yourself with ETL processes and tools like Airflow and Docker, as these are essential for the role.

Prepare for Behavioral Questions

KAYAK values cultural fit, so expect behavioral questions that assess your teamwork, problem-solving abilities, and adaptability. Reflect on your past experiences and be ready to discuss specific challenges you've faced, how you overcame them, and what you learned. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey your thought process clearly.

Engage with Your Interviewers

During your interviews, aim to create a dialogue rather than just answering questions. Show genuine interest in the team and the projects they are working on. Ask insightful questions about their work culture, team dynamics, and how they measure success. This not only demonstrates your enthusiasm for the role but also helps you gauge if KAYAK is the right fit for you.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the position and highlight any key points from the conversation that resonated with you. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

Be Patient and Persistent

The interview process at KAYAK can be lengthy, and communication may not always be prompt. If you haven’t heard back within the expected timeframe, it’s perfectly acceptable to send a polite follow-up email to inquire about your application status. Demonstrating patience and professionalism during this time reflects well on your character and can set you apart from other candidates.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at KAYAK. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at KAYAK. The interview process will likely assess your technical skills in data engineering, SQL, and Python, as well as your ability to work collaboratively with cross-functional teams. Be prepared to discuss your experience with data pipelines, data modeling, and problem-solving in a data context.

Technical Skills

1. Can you explain the process of building an ETL pipeline?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer role, as it is a core responsibility.

How to Answer

Outline the steps involved in building an ETL pipeline, emphasizing the importance of data extraction, transformation, and loading into a target system. Mention any tools or technologies you have used in the past.

Example

“Building an ETL pipeline involves first extracting data from various sources, such as databases or APIs. Next, I transform the data to ensure it meets the required format and quality standards, which may involve cleaning and aggregating the data. Finally, I load the transformed data into a target database or data warehouse, using tools like Apache Airflow for orchestration.”

2. What are some common SQL performance optimization techniques?

SQL optimization is key to ensuring efficient data retrieval and processing.

How to Answer

Discuss techniques such as indexing, query rewriting, and analyzing execution plans. Provide examples of when you applied these techniques in your previous work.

Example

“Common SQL performance optimization techniques include creating indexes on frequently queried columns to speed up data retrieval, rewriting complex queries to reduce execution time, and analyzing execution plans to identify bottlenecks. For instance, I once optimized a slow-running report by adding indexes and rewriting the query, which reduced the execution time from several minutes to under 30 seconds.”

3. How do you handle data quality issues?

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

How to Answer

Explain your process for identifying, diagnosing, and resolving data quality issues. Mention any tools or methodologies you use.

Example

“I handle data quality issues by first implementing validation checks during the ETL process to catch errors early. If issues arise, I investigate the root cause, whether it’s a data source problem or a transformation error. I also document these issues and work on preventive measures, such as improving data validation rules.”

4. Describe your experience with data visualization tools.

Data visualization is important for sharing insights with stakeholders.

How to Answer

Talk about the tools you have used, your experience in creating dashboards, and how you ensure the visualizations effectively communicate the data.

Example

“I have experience using tools like Tableau and Power BI to create dashboards that visualize key performance metrics. I focus on ensuring that the visualizations are intuitive and highlight the most important insights, allowing stakeholders to make data-driven decisions quickly.”

5. What is your experience with Python in data engineering?

Python is often used for data manipulation and ETL processes.

How to Answer

Discuss your proficiency in Python, particularly in relation to data engineering tasks, and any libraries you frequently use.

Example

“I have extensive experience using Python for data engineering tasks, particularly with libraries like Pandas for data manipulation and NumPy for numerical operations. I often write scripts to automate data processing tasks, which has significantly improved efficiency in my previous roles.”

Behavioral Questions

1. Tell me about a time you faced conflicting priorities in a project.

This question assesses your ability to manage multiple tasks and communicate effectively.

How to Answer

Describe the situation, how you prioritized tasks, and the outcome. Emphasize your communication skills and teamwork.

Example

“In a previous role, I was tasked with both developing a new data pipeline and supporting an ongoing project. I prioritized by assessing the deadlines and impact of each task, communicating with my team to delegate some responsibilities, and ensuring that both projects progressed smoothly. Ultimately, we met our deadlines without compromising quality.”

2. How do you ensure effective communication with cross-functional teams?

Collaboration is key in a data engineering role, and interviewers will want to know your approach.

How to Answer

Discuss your strategies for maintaining clear communication, such as regular updates, meetings, and using collaborative tools.

Example

“I ensure effective communication with cross-functional teams by scheduling regular check-ins and using project management tools like Jira to keep everyone updated on progress. I also encourage open dialogue, where team members can share their insights and concerns, fostering a collaborative environment.”

3. Why do you want to work for KAYAK?

This question assesses your motivation and fit for the company culture.

How to Answer

Express your interest in KAYAK’s mission and values, and how your skills align with their goals.

Example

“I want to work for KAYAK because I admire its commitment to innovation in the travel industry. I believe my skills in data engineering can contribute to enhancing user experiences and optimizing product performance, aligning perfectly with KAYAK’s mission to help people find their ideal travel options.”

4. Describe a challenging project you worked on and how you overcame the challenges.

This question evaluates your problem-solving skills and resilience.

How to Answer

Share a specific project, the challenges you faced, and the steps you took to overcome them.

Example

“I worked on a project where we needed to integrate multiple data sources into a single data warehouse. The challenge was ensuring data consistency and quality across sources. I overcame this by implementing a robust ETL process with thorough validation checks and collaborating closely with data owners to resolve discrepancies.”

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

This question assesses your commitment to professional development.

How to Answer

Discuss the resources you use to stay informed, such as online courses, blogs, or industry conferences.

Example

“I stay updated with the latest trends in data engineering by following industry blogs, participating in online forums, and attending webinars. I also take online courses to learn new tools and technologies, ensuring that my skills remain relevant in this rapidly evolving field.”