eBay Data Engineer Interview Questions + Guide in 2025

eBay is a global eCommerce leader revolutionizing the way individuals shop and sell, empowering millions of buyers and sellers in over 190 markets worldwide.

As a Data Engineer at eBay, your primary responsibility will be to enhance the reliability, scalability, and efficiency of the organization's data systems, particularly within the TCGplayer Technology team. You will play a pivotal role in designing and implementing data architectures that support the company's applications, ensuring optimal data flow and storage strategies. Key responsibilities include consulting on database design, optimizing query performance, and guiding cross-functional teams in the selection of datastore technologies that align with both current and future data needs.

The ideal candidate will possess extensive experience in designing datastores and data flows, mastery of MongoDB and RDBMS technologies, and a strong background in data governance practices. Excellent communication and leadership skills are essential, as you will mentor junior team members and collaborate with various stakeholders to drive data-related projects. This role aligns with eBay’s value of fostering innovation and creating economic opportunities for all, as you work to improve the effectiveness of data usage across the organization.

This guide will help you prepare by providing insights into the skills and competencies that eBay values in a Data Engineer, enabling you to demonstrate your fit for the role during your interviews.

The interview process for a Data Engineer role at eBay is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial HR Screening

The process begins with a phone interview conducted by a recruiter, lasting approximately 30 minutes. During this conversation, the recruiter will discuss your background, experience, and motivations for applying to eBay. This is also an opportunity for you to learn more about the company culture and the specifics of the Data Engineer role. The recruiter may ask about your familiarity with data engineering concepts and your experience with relevant technologies.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a case study or a coding challenge that tests your knowledge of SQL, Python, and statistics. The assessment is designed to evaluate your problem-solving skills and your ability to work with data. You may be asked to explain concepts such as HDFS and its effectiveness, as well as demonstrate your proficiency in writing database queries and designing data flows.

3. Technical Interviews

Candidates who successfully pass the technical assessment will move on to one or more technical interviews. These interviews typically involve discussions with senior data engineers or technical leads. Expect to delve deeper into your experience with data modeling, database design, and the specific technologies used at eBay, such as MongoDB and RDBMS. Interviewers may present you with real-world scenarios to assess your approach to solving data-related challenges and optimizing performance.

4. Behavioral Interviews

In addition to technical skills, eBay places a strong emphasis on cultural fit and collaboration. Behavioral interviews will focus on your past experiences, teamwork, and how you handle challenges in a fast-paced environment. Be prepared to discuss examples of how you have led projects, mentored others, and contributed to a positive team dynamic.

5. Final Interview

The final stage may involve a wrap-up interview with higher management or cross-functional team members. This is an opportunity for you to showcase your leadership skills and discuss your vision for data engineering within the organization. You may also be asked about your long-term career goals and how they align with eBay's mission and values.

As you prepare for your interviews, consider the specific skills and experiences that will be relevant to the questions you may encounter.

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

Understand the Role and Its Impact

As a Data Engineer at eBay, your role is pivotal in enhancing the efficiency and reliability of data systems. Familiarize yourself with how your work will directly impact the organization’s ability to serve millions of buyers and sellers. Be prepared to discuss how your previous experiences align with the responsibilities of optimizing data flow and datastore technologies, particularly with MongoDB and RDBMS like PostgreSQL.

Prepare for Technical Questions

Given the emphasis on SQL, statistics, and Python in the interview process, ensure you are well-versed in these areas. Brush up on SQL queries, focusing on complex joins, aggregations, and performance optimization techniques. For Python, be ready to demonstrate your ability to write clean, efficient code, particularly in the context of data manipulation and analysis. Additionally, review statistical concepts that may be relevant to data engineering tasks.

Showcase Your Problem-Solving Skills

During the interview, you may be presented with real-world scenarios or case studies. Approach these problems methodically: clarify the requirements, outline your thought process, and discuss potential solutions. Highlight your experience in designing scalable and efficient data architectures, and be prepared to explain your reasoning behind your design choices.

Communicate Effectively

eBay values strong communication skills, especially in a collaborative environment. Practice articulating your thoughts clearly and concisely. Be ready to discuss how you have successfully collaborated with cross-functional teams in the past, and emphasize your ability to influence and guide others through effective communication.

Embrace eBay's Culture

eBay promotes a culture of authenticity, innovation, and community. Reflect on how your personal values align with eBay’s mission and culture. Be prepared to share examples of how you have contributed to a positive team environment and how you embrace diverse perspectives in your work.

Follow Up Thoughtfully

After your interview, consider sending a follow-up email to express your gratitude for the opportunity and to reiterate your enthusiasm for the role. If you completed a case study or technical assessment, mention it briefly and highlight any key insights or learnings you gained from the process. This demonstrates your commitment and professionalism.

By focusing on these areas, you can present yourself as a strong candidate who not only possesses the technical skills required for the Data Engineer role but also embodies the values and culture that eBay cherishes. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at eBay. The interview will focus on your technical expertise in data engineering, particularly in SQL, statistics, and Python, as well as your ability to design scalable and efficient data architectures. Be prepared to discuss your experience with data modeling, database design, and your understanding of data governance.

SQL and Database Design

1. Can you explain the differences between SQL and NoSQL databases?

Understanding the distinctions between SQL and NoSQL databases is crucial for a Data Engineer, especially when discussing data storage solutions.

How to Answer

Discuss the fundamental differences in structure, scalability, and use cases for both types of databases. Highlight scenarios where one might be preferred over the other.

Example

“SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible, allowing for unstructured data and horizontal scaling, which is beneficial for applications with rapidly changing data requirements.”

2. Describe a time when you optimized a SQL query. What steps did you take?

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

How to Answer

Detail the specific query you optimized, the challenges you faced, and the techniques you used to improve performance, such as indexing or rewriting the query.

Example

“I had a SQL query that was taking too long to execute due to multiple joins. I analyzed the execution plan, added appropriate indexes, and simplified the query structure, which reduced the execution time from several minutes to under 10 seconds.”

3. What are window functions in SQL, and when would you use them?

Window functions are a powerful feature in SQL that can be useful for data analysis.

How to Answer

Explain what window functions are and provide examples of scenarios where they can be applied, such as calculating running totals or ranking data.

Example

“Window functions allow you to perform calculations across a set of table rows related to the current row. For instance, I used a window function to calculate the running total of sales for each month, which helped in analyzing trends over time without needing to group the data.”

4. How do you ensure data integrity in your database designs?

Data integrity is critical for maintaining accurate and reliable data.

How to Answer

Discuss the methods you use to enforce data integrity, such as constraints, normalization, and validation rules.

Example

“I ensure data integrity by implementing primary and foreign key constraints, using transactions to maintain atomicity, and applying validation rules at both the application and database levels to prevent invalid data entry.”

5. Explain the concept of normalization and denormalization. When would you use each?

This question tests your understanding of database design principles.

How to Answer

Define both concepts and provide examples of when you would choose to normalize or denormalize data based on performance and data retrieval needs.

Example

“Normalization involves organizing data to reduce redundancy, which is useful for transactional systems. However, in analytical systems where read performance is critical, I might denormalize data to speed up query performance, accepting some redundancy for the sake of efficiency.”

Statistics and Data Analysis

1. What statistical methods do you commonly use in data analysis?

This question gauges your familiarity with statistical concepts relevant to data engineering.

How to Answer

Mention specific statistical methods you have used, such as regression analysis, hypothesis testing, or A/B testing, and their applications.

Example

“I frequently use regression analysis to identify relationships between variables and A/B testing to evaluate the impact of changes in our data pipelines on performance metrics.”

2. How do you handle missing or incomplete data in your datasets?

Handling missing data is a common challenge in data engineering.

How to Answer

Discuss the strategies you employ to address missing data, such as imputation, removal, or using algorithms that can handle missing values.

Example

“When faced with missing data, I first assess the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques to fill in gaps or remove records if the missing data is not significant enough to impact the analysis.”

3. Can you explain the concept of data bias and how to mitigate it?

Understanding data bias is essential for ensuring fair and accurate data analysis.

How to Answer

Define data bias and discuss methods to identify and mitigate it, such as diversifying data sources or using bias detection algorithms.

Example

“Data bias occurs when certain groups are overrepresented or underrepresented in a dataset. To mitigate this, I ensure that my data sources are diverse and representative of the population, and I regularly audit my datasets for potential biases.”

4. Describe a statistical model you have built and its impact.

This question allows you to showcase your practical experience with statistical modeling.

How to Answer

Provide details about the model, the data used, and the outcomes or insights derived from it.

Example

“I built a logistic regression model to predict customer churn based on historical data. The model identified key factors influencing churn, allowing the marketing team to implement targeted retention strategies, which reduced churn by 15% over six months.”

5. How do you validate the results of your data analysis?

Validation is crucial for ensuring the reliability of your findings.

How to Answer

Discuss the techniques you use for validation, such as cross-validation, peer reviews, or comparing results against known benchmarks.

Example

“I validate my analysis results by using cross-validation techniques to ensure the model's robustness and comparing the outcomes with historical data to check for consistency and accuracy.”

Python and Data Manipulation

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

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

How to Answer

Mention popular libraries such as Pandas, NumPy, and Dask, and describe their use cases.

Example

“I frequently use Pandas for data manipulation and analysis due to its powerful DataFrame structure. For larger datasets, I utilize Dask to handle out-of-core computations efficiently.”

2. Can you describe a project where you used Python to automate a data pipeline?

This question allows you to demonstrate your practical experience with Python in data engineering.

How to Answer

Detail the project, the challenges faced, and how you used Python to automate processes.

Example

“I developed a Python script to automate the extraction, transformation, and loading (ETL) of data from various sources into our data warehouse. This reduced manual effort by 80% and improved data freshness for reporting.”

3. How do you handle exceptions and errors in your Python code?

Error handling is crucial for maintaining robust data pipelines.

How to Answer

Discuss the strategies you use for exception handling, such as try-except blocks and logging.

Example

“I use try-except blocks to catch exceptions and log errors for further analysis. This allows me to identify issues quickly and ensure that the data pipeline continues to run smoothly without crashing.”

4. Explain how you would use Python to connect to a database and retrieve data.

This question tests your practical knowledge of database interactions using Python.

How to Answer

Describe the libraries you would use and the steps involved in connecting to a database and executing queries.

Example

“I typically use the SQLAlchemy library to connect to databases in Python. I establish a connection, create a session, and then execute SQL queries to retrieve the necessary data, which I can then manipulate using Pandas.”

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

Data visualization is an important aspect of data analysis.

How to Answer

Mention the libraries you use for visualization and provide examples of how you have used them to present data insights.

Example

“I often use Matplotlib and Seaborn for data visualization in Python. For instance, I created visualizations to illustrate trends in user engagement, which helped stakeholders make informed decisions about product features.”