Tickets.Com Data Engineer Interview Questions + Guide in 2025

Tickets.Com, an innovative technology company under the MLB umbrella, is dedicated to enhancing fan experiences through cutting-edge digital solutions in the live sports and entertainment industry.

The Data Engineer role at Tickets.Com is pivotal in building and maintaining robust data pipelines that integrate diverse fan interaction and transaction data from multiple sources into the Data Warehouse. Key responsibilities include developing tools and processes for data management, enhancing existing data pipelines, and mentoring junior engineers. Candidates should possess extensive experience in cloud data warehouses, SQL, and Python, as well as a strong understanding of data engineering principles. A commitment to collaboration and a passion for using data to drive fan engagement and operational efficiency are essential traits for success in this role.

This guide will help you prepare effectively for your interview by providing insights into the skills and knowledge areas that are crucial for the Data Engineer position at Tickets.Com.

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

1. Initial HR Screening

The process begins with an initial screening call with an HR recruiter. This conversation is generally relaxed and serves as an opportunity for the recruiter to gauge your interest in the role, discuss your background, and provide insights into the company culture. You may also discuss your career aspirations and how they align with the mission of Tickets.com.

2. Technical Assessment

Following the HR screening, candidates typically participate in a technical assessment, which may be conducted via video call. This assessment focuses heavily on SQL and Python, as these are critical skills for the role. You will be presented with a problem statement that requires you to demonstrate your coding abilities and problem-solving skills. Be prepared for a hands-on coding exercise where you may need to write SQL queries or Python scripts to solve specific data-related challenges.

3. Team Interviews

Candidates who successfully pass the technical assessment will move on to interviews with members of the Data Engineering team. These interviews often involve multiple rounds where you will meet with various team members, including senior engineers and possibly the team lead. The focus here will be on your technical expertise, particularly in building and maintaining data pipelines, as well as your experience with cloud data warehouses and large data sets. Expect to discuss your previous projects and how you have applied your skills in real-world scenarios.

4. Final Interview with Leadership

The final round typically involves an interview with a senior leader or the VP of the Data Engineering team. This is an opportunity for you to showcase your leadership potential, discuss your vision for the role, and how you can contribute to the team's goals. You may also be asked about your experience mentoring junior engineers and your approach to building collaborative team environments.

Throughout the process, candidates should be prepared to discuss their familiarity with tools and technologies relevant to the role, such as Google BigQuery, Apache Spark, and DevOps practices.

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

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

Prepare for Technical Assessments

Given the emphasis on SQL and Python in the role, it's crucial to brush up on your technical skills. Expect to face multiple rounds of technical assessments, including SQL queries and Python coding challenges. Familiarize yourself with common SQL functions, especially those that are not aggregate functions, as well as Python libraries that are commonly used for data manipulation and API interactions. Practice coding problems that require you to think critically and solve real-world data engineering challenges.

Understand the Company’s Data Ecosystem

Tickets.com operates within a unique data environment, integrating fan interaction and transaction data from various sources. Research how the company utilizes data to enhance fan experiences and optimize ticketing operations. Understanding the specific data sources and the types of analytics performed will help you articulate how your skills can contribute to their mission. Be prepared to discuss how you would approach building and maintaining data pipelines in this context.

Embrace the DevOps Mindset

The role requires a DevOps mentality, so be ready to discuss your experience with CI/CD processes and infrastructure as code. Familiarize yourself with tools like Jenkins, Terraform, and Docker, as these are likely to come up in conversation. Highlight any experience you have with cloud platforms, particularly Google Cloud, and how you have implemented best practices in deploying and managing data applications.

Showcase Your Leadership Skills

As a Senior Data Engineer, you will be expected to mentor junior engineers and lead projects. Prepare examples from your past experiences where you have taken on leadership roles, whether through mentoring, code reviews, or leading a project. Demonstrating your ability to guide and support others will resonate well with the interviewers.

Communicate Clearly and Confidently

During the interview, clear communication is key. The interviewers may not be familiar with your previous work, so explain your thought process and decisions as you work through technical problems. If you encounter a question or problem you’re unsure about, don’t hesitate to think aloud and discuss your reasoning. This shows your problem-solving approach and willingness to engage in collaborative discussions.

Follow Up Thoughtfully

After the interview, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your enthusiasm for the role. If you don’t hear back within the expected timeline, a polite follow-up can demonstrate your continued interest. However, be mindful of the feedback from previous candidates regarding delayed responses, and approach follow-ups with patience.

By preparing thoroughly and aligning your skills and experiences with the company’s needs, you can position yourself as a strong candidate for the Data Engineer role at Tickets.com. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Tickets.com. The interview process will likely focus on your technical skills in SQL and Python, as well as your experience with data pipelines and cloud data warehouses. Be prepared to demonstrate your problem-solving abilities and your understanding of data engineering principles.

SQL

1. Describe how you would get the median by group in SQL, given that median isn't an aggregate function.

This question tests your understanding of SQL functions and your ability to manipulate data to derive insights.

How to Answer

Explain the approach you would take to calculate the median, such as using window functions or subqueries to rank the data and then select the appropriate value.

Example

“To calculate the median by group, I would first use a window function to assign a rank to each row within the group based on the value. Then, I would use a common table expression (CTE) to select the middle value(s) based on the rank. If the count of values is odd, I would select the middle value; if even, I would average the two middle values.”

2. How do you optimize SQL queries for performance?

This question assesses your knowledge of SQL optimization techniques.

How to Answer

Discuss various strategies such as indexing, query rewriting, and analyzing execution plans to improve performance.

Example

“I optimize SQL queries by first analyzing the execution plan to identify bottlenecks. I then consider adding indexes on frequently queried columns and rewriting the query to reduce complexity. Additionally, I ensure that I’m only selecting the necessary columns and using appropriate joins to minimize data processing.”

3. Can you explain the difference between INNER JOIN and LEFT JOIN?

This question evaluates your understanding of SQL joins and their implications on data retrieval.

How to Answer

Clearly define both types of joins and provide examples of when each would be used.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there’s no match, NULL values are returned for columns from the right table. I would use INNER JOIN when I only need records that exist in both tables, and LEFT JOIN when I want to retain all records from the left table regardless of matches.”

4. What are window functions in SQL, and how do you use them?

This question tests your knowledge of advanced SQL features.

How to Answer

Explain what window functions are and provide examples of their use cases.

Example

“Window functions perform calculations across a set of table rows that are related to the current row. For instance, I might use the ROW_NUMBER() function to assign a unique sequential integer to rows within a partition of a result set, which is useful for ranking data without collapsing the result set.”

Python

1. How do you handle exceptions in Python?

This question assesses your understanding of error handling in Python.

How to Answer

Discuss the use of try-except blocks and how you would log or manage exceptions.

Example

“I handle exceptions in Python using try-except blocks. I wrap the code that may raise an exception in a try block and then catch specific exceptions in the except block. I also log the error details to help with debugging. For example, if I’m making an API call, I would catch connection errors and log them for further investigation.”

2. Can you explain how you would interact with an API using Python?

This question evaluates your experience with APIs and data retrieval.

How to Answer

Describe the libraries you would use and the steps involved in making an API request.

Example

“To interact with an API in Python, I typically use the requests library. I would send a GET or POST request to the API endpoint, handle the response, and parse the JSON data returned. For example, I would use response = requests.get(url) and then check response.status_code to ensure the request was successful before processing the data.”

3. What libraries do you commonly use for data manipulation in Python?

This question tests your familiarity with Python libraries relevant to data engineering.

How to Answer

Mention libraries like Pandas and NumPy, and explain their use cases.

Example

“I commonly use Pandas for data manipulation and analysis due to its powerful DataFrame structure, which allows for easy data cleaning and transformation. I also use NumPy for numerical operations, especially when dealing with large datasets that require efficient computation.”

4. How do you automate tasks using Python?

This question assesses your ability to use Python for automation.

How to Answer

Discuss the types of tasks you have automated and the libraries or frameworks you used.

Example

“I automate tasks in Python by writing scripts that can perform repetitive actions, such as data extraction and transformation. For instance, I’ve used the schedule library to run scripts at specific intervals, and I often combine it with requests to pull data from APIs automatically.”

Data Engineering Concepts

1. What is ETL, and how does it differ from ELT?

This question evaluates your understanding of data processing methodologies.

How to Answer

Define ETL and ELT, and explain the differences in their processes and use cases.

Example

“ETL stands for Extract, Transform, Load, where data is transformed before loading it into the target system. ELT, on the other hand, stands for Extract, Load, Transform, where data is loaded first and then transformed within the target system. I prefer ELT when working with cloud data warehouses like BigQuery, as it allows for more flexibility in handling large datasets.”

2. How do you ensure data quality in your pipelines?

This question assesses your approach to maintaining data integrity.

How to Answer

Discuss methods for validating and cleaning data throughout the pipeline.

Example

“I ensure data quality by implementing validation checks at various stages of the pipeline. This includes schema validation, checking for duplicates, and ensuring that data falls within expected ranges. I also use logging to track data quality issues and set up alerts for anomalies.”

3. Can you explain the concept of data partitioning and its benefits?

This question tests your knowledge of data storage optimization techniques.

How to Answer

Define data partitioning and discuss its advantages in data processing.

Example

“Data partitioning involves dividing a dataset into smaller, manageable pieces based on certain criteria, such as date or region. This improves query performance and reduces processing time, as only relevant partitions are scanned during data retrieval. It’s particularly beneficial in large datasets where full scans would be inefficient.”

4. What is your experience with cloud data warehouses?

This question evaluates your familiarity with cloud technologies relevant to the role.

How to Answer

Discuss your experience with specific cloud platforms and how you’ve utilized them in data engineering.

Example

“I have extensive experience with Google BigQuery, where I’ve built and optimized data pipelines for large datasets. I appreciate its scalability and ability to handle complex queries efficiently. Additionally, I’ve worked with AWS Redshift for data warehousing and have implemented best practices for performance tuning.”