Zillow Data Engineer Interview Questions + Guide in 2025

Zillow is at the forefront of reimagining the real estate experience, empowering people to unlock life's next chapter through innovative digital solutions.

As a Data Engineer at Zillow, you will be a crucial part of the Analytics Tooling team, responsible for designing and developing processes that consolidate and analyze vast amounts of unstructured data to derive actionable insights. Your key responsibilities will include engaging with internal stakeholders to understand their analytical needs, implementing algorithms and automated processes, and utilizing your expertise in database structures and programming languages such as Java or Python. A background in cloud services and big data technologies will be beneficial, enabling you to effectively collaborate with data scientists, product managers, and analysts. The ideal candidate should possess strong problem-solving skills, a passion for data and analytics, and the ability to thrive in a dynamic, evolving environment.

This guide is designed to help you prepare for a job interview by providing insights into the expectations and requirements of the role, equipping you with the knowledge to stand out among other candidates.

The interview process for a Data Engineer role at Zillow is designed to assess both technical skills and cultural fit within the team. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Phone Interview

The process begins with an initial phone interview, usually lasting around 30 minutes. This conversation is typically conducted by a recruiter who will discuss the role, the company culture, and your background. Expect to answer questions about your experience with data engineering, programming languages like Python or Java, and your familiarity with data tools and frameworks. This is also an opportunity for you to ask questions about the team and the work environment at Zillow.

2. Technical Assessment

Following the initial screen, candidates will undergo a technical assessment, which may be conducted via video call. This stage often includes coding challenges, such as algorithmic problems or data manipulation tasks, to evaluate your problem-solving skills and proficiency in programming. You may also be asked to demonstrate your understanding of data structures and algorithms, as well as your ability to write clean, efficient code.

3. Onsite Interviews

The onsite interview typically consists of multiple rounds, often including two algorithm-focused interviews and two system design interviews. During these sessions, you will be expected to solve complex problems in real-time, showcasing your analytical thinking and technical expertise. Additionally, you may have interviews with hiring managers or team leads, where you will discuss your previous projects, your approach to data engineering challenges, and how you collaborate with cross-functional teams.

4. Behavioral Interviews

In conjunction with the technical rounds, there will be behavioral interviews aimed at assessing your interpersonal skills and cultural fit within Zillow. These interviews will focus on your past experiences, how you handle challenges, and your ability to work collaboratively in a team environment. Be prepared to share specific examples that highlight your problem-solving abilities and your passion for data and analytics.

5. Final Discussions

The final stage may involve discussions with senior leadership or additional team members to ensure alignment with Zillow's values and mission. This is also a chance for you to ask any remaining questions about the role, team dynamics, and future projects.

As you prepare for your interview, consider the types of questions that may arise during these various stages.

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

Embrace the Collaborative Culture

Zillow places a strong emphasis on collaboration and teamwork. During your interview, be prepared to discuss your experiences working with cross-functional teams, particularly with analysts, product managers, and data scientists. Highlight specific instances where you successfully collaborated to solve complex problems or drive projects forward. This will demonstrate your alignment with Zillow's culture of support and innovation.

Prepare for Technical Depth

Expect to face a mix of technical questions, including algorithm challenges and system design scenarios. Brush up on your knowledge of data structures, algorithms, and system architecture. Familiarize yourself with the technologies mentioned in the job description, such as Python, Java, and big data tools like Spark and Kafka. Practicing coding problems on platforms like LeetCode can help you feel more confident and prepared.

Showcase Your Problem-Solving Skills

Zillow values candidates who are curious and passionate problem-solvers. Be ready to discuss how you approach complex data challenges and the methodologies you use to derive insights from large datasets. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate the impact of your solutions.

Highlight Your Adaptability

Given the fast-paced nature of the tech industry, Zillow appreciates candidates who can adapt to new technologies and changing environments. Share examples of how you've successfully navigated change in previous roles, whether it was adopting new tools, processes, or methodologies. This will illustrate your ability to thrive in a dynamic setting.

Communicate Clearly and Effectively

Strong communication skills are essential for this role, especially since you'll be interacting with various stakeholders. Practice articulating your thoughts clearly and concisely, both in technical discussions and when explaining complex concepts to non-technical audiences. This will help you stand out as a candidate who can bridge the gap between technical and non-technical teams.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your interpersonal skills and cultural fit. Prepare to discuss your experiences in a way that reflects Zillow's values, such as equity, belonging, and innovation. Think about how your personal values align with the company's mission to empower people and improve the real estate experience.

Engage with Your Interviewers

During the interview, take the opportunity to engage with your interviewers by asking insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you gauge if Zillow is the right fit for you. Be genuine in your curiosity, and don’t hesitate to share your thoughts on how you can contribute to their mission.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Zillow. The interview process will likely assess your technical skills, problem-solving abilities, and your capacity to collaborate with cross-functional teams. Be prepared to demonstrate your knowledge in data engineering, system design, and your experience with relevant technologies.

Technical Skills

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

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the use cases for each type of database, highlighting their scalability, structure, and performance characteristics.

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 handling large volumes of data with varying formats."

2. Describe a data pipeline you have built. What were the challenges you faced?

This question assesses your practical experience in building data pipelines.

How to Answer

Detail the architecture of the pipeline, the technologies used, and the specific challenges encountered, along with how you overcame them.

Example

"I built a data pipeline using Apache Airflow to automate the ETL process for our sales data. One challenge was ensuring data quality, so I implemented validation checks at each stage, which significantly reduced errors in our reporting."

3. How do you ensure data quality and integrity in your projects?

Data quality is paramount in data engineering roles, and interviewers want to know your approach.

How to Answer

Discuss the methods and tools you use to monitor and maintain data quality throughout the data lifecycle.

Example

"I implement data validation rules and use tools like Great Expectations to automate data quality checks. Additionally, I regularly conduct audits and maintain documentation to ensure transparency and traceability."

4. What is your experience with cloud services, and how have you utilized them in your projects?

Cloud services are integral to modern data engineering, and familiarity with them is essential.

How to Answer

Mention specific cloud platforms you have worked with and how they were applied in your projects.

Example

"I have extensive experience with AWS, particularly using S3 for data storage and Redshift for data warehousing. I leveraged these services to create a scalable architecture that improved our data retrieval times by 30%."

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

Data partitioning is a key concept in managing large datasets effectively.

How to Answer

Explain what data partitioning is and how it can improve performance and manageability.

Example

"Data partitioning involves dividing a dataset into smaller, more manageable pieces. This approach enhances query performance by allowing parallel processing and reduces the load on any single resource, which is especially beneficial for large-scale data operations."

System Design

1. How would you design a system to handle real-time data processing?

This question evaluates your system design skills and understanding of real-time data processing.

How to Answer

Outline the components of your system, including data ingestion, processing, and storage, and justify your choices.

Example

"I would use Apache Kafka for data ingestion to handle high throughput, followed by Apache Flink for real-time processing. The processed data would be stored in a NoSQL database like Cassandra for quick access, ensuring low latency for end-users."

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

This question assesses your problem-solving skills in a practical scenario.

How to Answer

Detail the steps you took to identify the issue and the optimizations you implemented.

Example

"I noticed a query was taking too long due to a lack of indexing. I analyzed the query execution plan, added appropriate indexes, and restructured the query to reduce complexity, which improved performance by over 50%."

3. What considerations do you take into account when designing a data model?

This question tests your understanding of data modeling principles.

How to Answer

Discuss the factors that influence your data model design, such as scalability, normalization, and access patterns.

Example

"When designing a data model, I consider the expected data volume, access patterns, and the need for normalization versus denormalization. I aim for a balance that supports efficient querying while maintaining data integrity."

4. How do you approach version control in your data engineering projects?

Version control is essential for collaboration and maintaining code quality.

How to Answer

Explain your preferred tools and practices for version control in data engineering.

Example

"I use Git for version control, ensuring that all code changes are tracked. I follow a branching strategy that allows for feature development without disrupting the main codebase, and I conduct code reviews to maintain quality."

5. Can you describe a situation where you had to collaborate with data scientists or analysts?

Collaboration is key in data engineering, and this question assesses your interpersonal skills.

How to Answer

Share an example of a project where you worked closely with data scientists or analysts, focusing on communication and problem-solving.

Example

"I collaborated with data scientists to develop a predictive model. I ensured they had access to clean, well-structured data by building a robust data pipeline. Regular meetings helped us align on requirements and iterate quickly on the model."