Sabre Data Engineer Interview Questions + Guide in 2025

Sabre is a technology company that provides software solutions and services to the global travel and tourism industry, empowering businesses to make data-driven decisions.

As a Data Engineer at Sabre, you will play a critical role in designing, building, and maintaining the data architecture that supports the company's diverse applications and services. Your key responsibilities will include developing scalable data pipelines, ensuring data quality and integrity, and collaborating with cross-functional teams to provide data solutions that meet business needs. A strong understanding of database management systems, data warehousing, and big data technologies is essential, as you will be tasked with transforming raw data into actionable insights.

Candidates who thrive in this role possess strong analytical skills, a proficiency in programming languages such as SQL and Python, and experience with data modeling and ETL processes. Additionally, familiarity with cloud services and a keen interest in the latest data trends will set you apart. At Sabre, we value innovation, collaboration, and a commitment to excellence, making these traits vital for success in this position.

This guide will help you prepare for your interview by providing insights into the skills and knowledge areas that are crucial for the Data Engineer role at Sabre, as well as tips on how to effectively showcase your abilities during the interview process.

The interview process for a Data Engineer role at Sabre is structured and typically consists of multiple stages designed to assess both technical and interpersonal skills.

1. Online Assessment

The first step in the interview process is an online assessment that evaluates candidates' problem-solving abilities, coding skills, and foundational knowledge in computer science. This assessment usually includes multiple-choice questions covering topics such as data structures, algorithms, operating systems, and database management systems, along with a couple of coding challenges that candidates must complete within a specified time frame.

2. Technical Interview

Candidates who successfully pass the online assessment will move on to a technical interview. This round is typically conducted by experienced engineers and focuses on in-depth discussions about the candidate's technical knowledge and problem-solving approach. Expect questions related to data engineering concepts, coding exercises, and possibly system design scenarios. Candidates may also be asked to explain their previous projects and the technologies they used.

3. Managerial Interview

Following the technical interview, candidates will participate in a managerial round. This interview assesses the candidate's ability to handle real-world scenarios and evaluates their leadership, decision-making, and teamwork skills. Questions may revolve around past experiences, how candidates approach challenges, and their alignment with Sabre's values and objectives.

4. HR Interview

The final stage of the interview process is the HR interview. This round typically involves discussions about compensation, benefits, company culture, and career growth opportunities. It also serves as a platform for the HR team to evaluate the candidate's enthusiasm for joining Sabre and their cultural fit within the organization.

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

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

Understand the Technical Landscape

As a Data Engineer at Sabre, you will be working in a network-based environment, so it's crucial to have a solid understanding of networking concepts. Brush up on your knowledge of data structures, algorithms, and database management systems (DBMS). Familiarize yourself with the specific technologies and tools that Sabre uses, as this will demonstrate your genuine interest in the role and the company.

Prepare for Coding Challenges

Expect to face coding challenges during the interview process. Practice solving problems related to data structures and algorithms, as well as SQL queries. Be prepared to explain your thought process while coding, as interviewers often look for clarity in your approach. Utilize platforms like LeetCode or HackerRank to sharpen your skills and get comfortable with coding under time constraints.

Master the Behavioral Questions

Sabre values cultural fit, so be ready to answer behavioral questions that assess your problem-solving abilities and teamwork skills. Reflect on your past experiences and prepare examples that showcase your analytical skills, adaptability, and how you handle challenges. Questions like "Why do you want to work at Sabre?" or "Where do you see yourself in five years?" are common, so have thoughtful responses ready.

Engage in Technical Discussions

During technical interviews, you may be asked to explain your previous projects or the technologies you've used. Be prepared to discuss your contributions in detail, including the challenges you faced and how you overcame them. This not only demonstrates your technical expertise but also your ability to communicate effectively.

Be Ready for a Presentation

Some candidates have reported a presentation round as part of the interview process. If this applies to you, choose a topic that highlights your skills and experience relevant to the role. Practice your presentation skills to ensure you can convey your ideas clearly and confidently.

Stay Calm and Professional

Interviews can be nerve-wracking, but maintaining a calm demeanor can significantly impact your performance. Approach each round with confidence, and remember that the interviewers are looking for the right fit for their team. If you encounter difficult questions, take a moment to think before responding, and don’t hesitate to ask for clarification if needed.

Follow Up

After your interview, consider sending a thank-you email to express your appreciation for the opportunity. This not only reinforces your interest in the position but also leaves a positive impression on the interviewers.

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

Technical Skills

1. Can you explain the difference between a linked list and an array?

Understanding data structures is crucial for a Data Engineer role, as it impacts how data is stored and accessed.

How to Answer

Discuss the key differences in terms of memory allocation, access time, and use cases for each structure.

Example

“A linked list allows for dynamic memory allocation, meaning it can grow and shrink as needed, while an array has a fixed size. This makes linked lists more flexible for certain applications, but arrays provide faster access times due to contiguous memory allocation.”

2. Describe a situation where you optimized a data processing pipeline. What changes did you implement?

This question assesses your practical experience in improving data workflows.

How to Answer

Focus on the specific changes you made, the tools you used, and the measurable impact of those changes.

Example

“I worked on a data pipeline that processed user logs. By implementing batch processing instead of real-time processing, I reduced the load on our servers by 30% and improved processing time by 50%.”

3. What are the different types of joins in SQL? Can you provide examples?

SQL proficiency is essential for data manipulation and retrieval.

How to Answer

Explain the types of joins (INNER, LEFT, RIGHT, FULL) and provide a brief example of each.

Example

“An INNER JOIN returns records that have matching values in both tables, while a LEFT JOIN returns all records from the left table and matched records from the right. For instance, if we have a table of customers and a table of orders, an INNER JOIN would show only customers who have placed orders, while a LEFT JOIN would show all customers, including those who haven’t placed any orders.”

4. How do you handle missing or corrupted data in a dataset?

Data integrity is critical, and this question evaluates your approach to data quality.

How to Answer

Discuss methods such as imputation, removal, or flagging of missing data, and the importance of documenting these decisions.

Example

“I typically assess the extent of missing data first. If it’s minimal, I might use imputation techniques to fill in gaps. For larger issues, I may choose to remove those records entirely, ensuring to document the rationale for future reference.”

5. Can you explain the concept of normalization in databases? Why is it important?

Normalization is a key concept in database design that ensures data integrity.

How to Answer

Define normalization and discuss its benefits, such as reducing redundancy and improving data integrity.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. For example, by separating customer information into a different table, we can avoid duplicating data across multiple records, which simplifies updates and reduces the risk of inconsistencies.”

Data Structures and Algorithms

1. What is a binary tree, and how does it differ from a binary search tree?

This question tests your understanding of fundamental data structures.

How to Answer

Define both structures and highlight their differences in terms of organization and use cases.

Example

“A binary tree is a tree data structure where each node has at most two children. A binary search tree is a type of binary tree where the left child contains values less than the parent node, and the right child contains values greater, which allows for efficient searching.”

2. Can you describe a time when you had to implement a complex algorithm? What challenges did you face?

This question assesses your problem-solving skills and experience with algorithms.

How to Answer

Share a specific example, focusing on the algorithm used, the problem it solved, and any obstacles encountered.

Example

“I implemented Dijkstra’s algorithm to find the shortest path in a network of servers. The challenge was optimizing the algorithm for large datasets, which I addressed by using a priority queue to improve efficiency.”

3. How would you approach solving a problem using dynamic programming?

Dynamic programming is a critical concept in algorithm design.

How to Answer

Explain the principles of dynamic programming and provide a brief example of a problem you solved using this approach.

Example

“I approach dynamic programming by breaking down a problem into smaller subproblems and storing their results to avoid redundant calculations. For instance, I used this method to solve the Fibonacci sequence problem, storing previously calculated values to improve performance.”

4. What is the time complexity of common sorting algorithms?

Understanding time complexity is essential for evaluating algorithm efficiency.

How to Answer

Discuss the time complexities of various sorting algorithms, such as Quick Sort, Merge Sort, and Bubble Sort.

Example

“Quick Sort has an average time complexity of O(n log n), while Merge Sort also has O(n log n) but is stable. Bubble Sort, on the other hand, has a time complexity of O(n^2), making it inefficient for large datasets.”

5. Can you explain what a hash table is and its advantages?

Hash tables are a fundamental data structure used for efficient data retrieval.

How to Answer

Define a hash table and discuss its advantages, such as average-case constant time complexity for lookups.

Example

“A hash table is a data structure that maps keys to values for efficient data retrieval. Its primary advantage is that it allows for average-case O(1) time complexity for lookups, making it ideal for scenarios where quick access to data is required.”