Glassdoor Data Engineer Interview Questions + Guide in 2025

Glassdoor is a platform dedicated to fostering radical transparency in the workplace by providing professionals with a space to share insights about their experiences, including company reviews, salary information, and job listings.

As a Data Engineer at Glassdoor, you will be at the forefront of transforming the company’s data infrastructure to support the growing engagement of its community offerings. This role involves re-architecting and designing data systems that are capable of handling both stream and batch processing, ensuring that data is accessible and reliable for various products, including Glassdoor and Fishbowl. Key responsibilities include implementing best practices in software development, establishing a data quality strategy, mentoring junior engineers, and conducting architecture reviews. Successful candidates will possess strong expertise in Python, data architecture, and cloud fundamentals, particularly within AWS environments, alongside a commitment to fostering an inclusive team culture.

This guide is designed to equip you with the knowledge to navigate the interview process effectively by providing insights into the key skills and responsibilities associated with the Data Engineer role at Glassdoor.

The interview process for a Data Engineer role at Glassdoor is designed to assess both technical expertise and cultural fit within the team. It typically consists of several structured steps that allow candidates to showcase their skills and experiences.

1. Initial Phone Interview

The first step in the interview process is a phone interview, usually conducted by a recruiter or the hiring manager. This conversation lasts about 30-45 minutes and focuses on your background, previous projects, and relevant technical skills. Expect to discuss your experience with data engineering, cloud technologies, and programming languages, particularly Python. The recruiter will also gauge your fit within Glassdoor's culture and values.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may be conducted via video conferencing and will delve deeper into your technical skills. You can expect questions related to algorithms, system design, and specific technologies relevant to the role, such as container orchestration tools (Docker, Kubernetes), CI/CD practices, and AWS services. The interviewer may also explore your experience with data processing frameworks and your understanding of data architecture.

3. Onsite Interview

The onsite interview consists of multiple rounds, usually four, where candidates meet with various team members, including senior engineers and the hiring manager. Each round lasts approximately 45 minutes and covers a mix of technical and behavioral questions. You will be assessed on your problem-solving abilities, coding skills, and your approach to data quality and integrity. Additionally, expect discussions around your past experiences and how they relate to the responsibilities of the Data Engineer role.

4. Final Interview

The final interview often involves a wrap-up session with the hiring manager or senior leadership. This is an opportunity for you to ask questions about the team, company culture, and future projects. It also serves as a chance for the interviewers to evaluate your overall fit for the team and your alignment with Glassdoor's mission and values.

As you prepare for your interview, 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.

Emphasize Your Technical Expertise

Given the role's focus on data engineering, it's crucial to showcase your proficiency in Python and your understanding of algorithms. Be prepared to discuss specific projects where you utilized these skills, particularly in cloud environments. Highlight any experience you have with container orchestration tools like Docker and Kubernetes, as well as CI/CD practices. The interviewers will likely appreciate concrete examples that demonstrate your ability to solve complex problems and optimize data processes.

Prepare for a Collaborative Environment

Glassdoor values a culture of collaboration and mentorship. During your interview, be ready to discuss how you've worked effectively in teams and contributed to a positive team dynamic. Share instances where you mentored others or led initiatives that improved team performance. This will resonate well with the interviewers, who are looking for candidates that can foster a supportive and inclusive work environment.

Understand the Company’s Mission and Values

Familiarize yourself with Glassdoor's commitment to transparency and community engagement. Reflect on how your personal values align with the company's mission to improve work life for everyone. Be prepared to articulate how you can contribute to this mission through your role as a Data Engineer. This alignment will demonstrate your genuine interest in the company and its goals.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This approach will help you provide clear and concise answers that highlight your thought process and the impact of your actions. Remember, the interviewers are looking for insights into your character and how you approach your work.

Engage with the Interviewers

The interview process at Glassdoor is described as friendly and respectful. Take this opportunity to engage with your interviewers by asking thoughtful questions about their experiences and the team dynamics. This not only shows your interest in the role but also helps you gauge if the company culture is a good fit for you.

Follow Up with Gratitude

After your interviews, send a thank-you email to your interviewers and recruiter. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This small gesture can leave a lasting impression and reinforce your interest in joining the Glassdoor team.

By following these tips, you'll be well-prepared to showcase your skills and fit for the Data Engineer role at Glassdoor. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Glassdoor. The interview process will likely focus on your technical skills, experience with data systems, and your ability to work collaboratively within a team. Be prepared to discuss your past projects, as well as demonstrate your knowledge of data engineering principles and best practices.

Technical Skills

1. Can you explain your experience with Python in data engineering projects?

This question aims to assess your proficiency in Python, which is crucial for data manipulation and processing.

How to Answer

Discuss specific projects where you utilized Python, emphasizing libraries or frameworks you used, such as Pandas or NumPy, and how they contributed to the project's success.

Example

“In my last project, I used Python with Pandas to clean and transform large datasets for analysis. This involved writing scripts to automate data extraction from various sources, which improved our data processing time by 30%.”

2. Describe your experience with container orchestration tools like Docker or Kubernetes.

This question evaluates your familiarity with modern deployment practices.

How to Answer

Highlight specific instances where you implemented Docker or Kubernetes, focusing on how they improved your workflow or project outcomes.

Example

“I have used Docker to containerize applications, which allowed for consistent environments across development and production. In one project, I set up a Kubernetes cluster to manage our microservices, which enhanced our scalability and reduced downtime during deployments.”

3. What strategies do you use to ensure data quality in your projects?

This question assesses your understanding of data integrity and quality assurance.

How to Answer

Discuss specific methodologies or tools you employ to maintain data quality, such as validation checks or automated testing.

Example

“I implement data validation checks at various stages of the ETL process to catch errors early. Additionally, I use tools like Great Expectations to automate data quality testing, ensuring that only clean data enters our production environment.”

4. Can you explain the role of Airflow in your data pipeline management?

This question tests your knowledge of workflow orchestration tools.

How to Answer

Describe how you have used Airflow to manage data workflows, including any custom operators you developed.

Example

“I have utilized Airflow to schedule and monitor our ETL processes. I developed custom operators to handle specific data transformations, which streamlined our workflows and provided better visibility into job statuses.”

5. How do you approach performance optimization in data processing?

This question evaluates your ability to enhance the efficiency of data systems.

How to Answer

Discuss techniques you have used to optimize performance, such as indexing, partitioning, or caching strategies.

Example

“In a recent project, I optimized our data processing by implementing partitioning in our Hive tables, which reduced query times significantly. I also utilized caching for frequently accessed data, which improved overall system performance.”

System Design

1. How would you design a data pipeline for a new product feature?

This question assesses your system design skills and ability to think critically about data architecture.

How to Answer

Outline the steps you would take to design the pipeline, including data sources, processing methods, and storage solutions.

Example

“I would start by identifying the data sources required for the new feature, then design an ETL pipeline using Airflow to extract, transform, and load the data into our data warehouse. I would ensure that the pipeline is scalable and can handle increased data loads as the feature gains traction.”

2. What considerations do you take into account when designing a data lake?

This question evaluates your understanding of data storage solutions.

How to Answer

Discuss aspects such as data governance, access control, and performance that you consider when designing a data lake.

Example

“When designing a data lake, I prioritize data governance by implementing strict access controls and metadata management. I also consider performance by choosing the right storage format, such as Parquet, to optimize query performance.”

3. Describe a challenging data architecture problem you faced and how you solved it.

This question tests your problem-solving skills in real-world scenarios.

How to Answer

Share a specific challenge, the steps you took to address it, and the outcome.

Example

“I faced a challenge with data duplication in our warehouse. I implemented a deduplication process using unique identifiers and adjusted our ETL jobs to include checks for existing records, which significantly improved our data integrity.”

4. How do you ensure scalability in your data systems?

This question assesses your ability to design systems that can grow with demand.

How to Answer

Discuss strategies you use to ensure that your data systems can handle increased loads.

Example

“I ensure scalability by designing modular data pipelines that can be easily replicated and by leveraging cloud services like AWS EMR, which allows us to scale resources up or down based on demand.”

5. What is your experience with data modeling, and how do you approach it?

This question evaluates your understanding of data structures and relationships.

How to Answer

Explain your approach to data modeling, including any tools or methodologies you use.

Example

“I approach data modeling by first understanding the business requirements and then creating an ERD to visualize the relationships between entities. I use tools like Lucidchart for this process and ensure that the model is flexible enough to accommodate future changes.”