Groupon Data Engineer Interview Questions + Guide in 2025

Groupon is a global marketplace connecting consumers with local merchants by offering unique deals and experiences.

As a Data Engineer at Groupon, you will play a pivotal role in building and maintaining the data infrastructure that supports data-driven decision-making across the organization. Your key responsibilities will include designing, constructing, and optimizing scalable data pipelines, ensuring data integrity and availability, and collaborating closely with data scientists and analysts to understand their data needs. A strong understanding of programming languages such as Python or Java, proficiency in SQL, and experience with data warehousing solutions are essential for success in this role. Additionally, excellent problem-solving skills and the ability to work collaboratively in a fast-paced environment will make you a great fit for Groupon's culture, which values innovation and teamwork.

This guide will help you prepare for your interview by providing you with insights into the role's expectations and the types of questions you may encounter, ultimately enhancing your confidence and performance during the interview process.

The interview process for a Data Engineer role at Groupon is structured to assess both technical skills and cultural fit within the team. The process typically unfolds as follows:

1. Initial Phone Screen

The first step in the interview process is an initial phone call with a recruiter. This conversation usually lasts around 30 minutes and focuses on discussing your resume, relevant experiences, and the specifics of the Data Engineer role. The recruiter will also provide insights into Groupon's culture and values, ensuring that you understand what it means to work at the company.

2. Technical Screening

Following the initial screen, candidates typically undergo a technical screening with the hiring manager. This session is often conducted via video call and lasts about an hour. During this interview, you can expect to tackle coding challenges, often sourced from platforms like LeetCode, which may include easy to moderate difficulty questions. The focus will be on your problem-solving abilities and coding proficiency, particularly in languages relevant to the role, such as Python.

3. Onsite Interview

The onsite interview is a more comprehensive evaluation, usually spanning around five hours. It consists of multiple back-to-back interviews with various team members, including engineers, product managers, and quality assurance personnel. The first interview is generally more relaxed, allowing you to ask questions about the role and team dynamics. Subsequent interviews will delve into behavioral questions, technical whiteboarding exercises, and hands-on coding tasks. Expect to demonstrate your SQL skills and discuss design approaches relevant to data engineering.

4. Final Discussions

After the onsite interviews, there may be a final discussion with the hiring manager to assess your fit within the team and clarify any remaining questions. This step is crucial for both parties to ensure alignment on expectations and team culture.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during this process.

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

Understand the Interview Process

Groupon's interview process typically involves multiple stages, starting with a phone call with a recruiter, followed by technical screenings and an on-site interview. Familiarize yourself with this structure so you can prepare accordingly. Expect a mix of behavioral and technical questions, including coding challenges and SQL queries. Knowing the flow of the interview will help you manage your time and energy effectively.

Prepare for Technical Assessments

As a Data Engineer, you will likely face coding challenges that require proficiency in languages such as Python and SQL. Brush up on your coding skills, particularly focusing on data manipulation and algorithmic problem-solving. Practice common LeetCode problems, especially those categorized as easy to moderate, as these are often the types of questions you may encounter. Additionally, be prepared to discuss your design approach for data systems, as this is a critical aspect of the role.

Emphasize Team Dynamics and Culture Fit

During your interviews, take the opportunity to ask questions about the team dynamics and company culture. Groupon values collaboration and a positive work environment, so demonstrating your interest in how you can contribute to the team will set you apart. Be genuine in your inquiries and share your thoughts on how you can align with their values.

Master the Whiteboard Challenge

Whiteboard coding is a common part of the interview process at Groupon. Practice articulating your thought process while solving problems on a whiteboard. This will not only help you communicate your ideas clearly but also showcase your problem-solving skills. Remember, the interviewers are interested in how you approach problems, so don’t hesitate to think aloud and explain your reasoning.

Follow Up Professionally

After your interviews, it’s important to maintain communication with the HR team. If you haven’t heard back within a reasonable timeframe, don’t hesitate to follow up politely. This shows your continued interest in the position and helps you stay informed about your application status. However, be mindful of their timelines and avoid excessive follow-ups.

Be Yourself

Lastly, be authentic during your interviews. Groupon values individuals who are not only technically proficient but also bring their unique perspectives to the table. Share your experiences, challenges, and successes candidly. This will help you connect with your interviewers and leave a lasting impression.

By following these tips, you’ll be well-prepared to navigate the interview process at Groupon and demonstrate your fit for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Groupon. The interview process will assess your technical skills, problem-solving abilities, and understanding of data engineering principles. Be prepared to discuss your experience with data pipelines, database management, and coding challenges.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is fundamental to data integration and management.

How to Answer

Discuss the steps involved in ETL and how they contribute to data quality and accessibility. Highlight any experience you have with ETL tools or frameworks.

Example

“The ETL process involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. This process is vital for ensuring that data is clean, consistent, and readily available for analysis. In my previous role, I utilized Apache NiFi to automate ETL workflows, which significantly improved our data processing efficiency.”

2. Describe a challenging data pipeline you built. What were the key considerations?

This question assesses your practical experience and problem-solving skills in building data pipelines.

How to Answer

Focus on the challenges you faced, the technologies you used, and how you ensured the pipeline was efficient and reliable.

Example

“I built a data pipeline that ingested real-time data from multiple APIs. The key considerations included handling data latency, ensuring data integrity, and scaling the pipeline to accommodate increasing data volumes. I used Apache Kafka for real-time data streaming and implemented monitoring tools to track performance and errors.”

3. What is your experience with SQL? Can you write a query to find duplicate records in a table?

SQL proficiency is essential for a Data Engineer, and this question tests your ability to write effective queries.

How to Answer

Demonstrate your SQL knowledge by explaining the query structure and logic. Be prepared to write the query on a whiteboard.

Example

“I have extensive experience with SQL, including writing complex queries for data analysis. To find duplicate records, I would use a query like: SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name HAVING COUNT(*) > 1; This query groups records by the specified column and counts occurrences, returning only those with duplicates.”

4. How do you ensure data quality in your projects?

Data quality is a critical aspect of data engineering, and this question evaluates your approach to maintaining it.

How to Answer

Discuss the methods and tools you use to validate and clean data, as well as any frameworks you follow.

Example

“I ensure data quality by implementing validation checks at various stages of the data pipeline. I use tools like Great Expectations for data validation and regularly conduct data audits to identify and rectify inconsistencies. Additionally, I advocate for a culture of data stewardship within the team to promote accountability.”

Programming and Coding

5. What programming languages are you proficient in, and how have you used them in data engineering?

This question assesses your coding skills and familiarity with relevant programming languages.

How to Answer

Mention the languages you are comfortable with and provide examples of how you have applied them in your work.

Example

“I am proficient in Python and Java, which I have used extensively for data manipulation and building data pipelines. For instance, I developed a data processing application in Python using Pandas to clean and analyze large datasets, which improved our reporting accuracy.”

6. Can you explain the concept of data normalization and its benefits?

Understanding data normalization is important for database design and management.

How to Answer

Define data normalization and discuss its advantages in reducing redundancy and improving data integrity.

Example

“Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. By structuring data into related tables, we can ensure that updates are made consistently and that the database remains efficient. For example, in a customer database, normalizing data would involve separating customer information from order details to avoid duplication.”

7. Describe a time when you had to optimize a slow-running query. What steps did you take?

This question evaluates your problem-solving skills and understanding of database performance.

How to Answer

Discuss the specific query, the performance issues you encountered, and the optimization techniques you applied.

Example

“I encountered a slow-running query that was causing delays in our reporting system. I analyzed the query execution plan and identified missing indexes as a key issue. After adding the necessary indexes and rewriting the query to reduce complexity, I was able to improve its performance by over 50%.”

8. What tools or frameworks do you use for data orchestration?

This question assesses your familiarity with data orchestration tools, which are essential for managing data workflows.

How to Answer

Mention the tools you have experience with and how they have helped you in your projects.

Example

“I have used Apache Airflow for data orchestration, which allows me to schedule and monitor complex workflows. By defining Directed Acyclic Graphs (DAGs), I can manage dependencies between tasks and ensure that data pipelines run smoothly and efficiently.”