At Zillow, data is the foundation of every product, decision, and user experience. As a Data Engineer, you will design, build, and maintain the highly scalable data pipelines and platforms that power the most visited real estate network in the United States. Your work directly impacts millions of buyers, sellers, and renters by ensuring that property listings, historical transactions, and market trends are processed accurately and delivered in real time.

You will work on problems of massive scale, handling petabytes of spatial, geographic, and financial data. Whether you are optimizing the data ingestion pipelines that feed the world-famous Zestimate algorithm, structuring data lakes to support complex machine learning models, or building robust analytics platforms for internal business partners, your technical contributions will have a direct, visible influence on the real estate market.

This role requires a unique blend of software engineering discipline and data architecture expertise. Zillow looks for engineers who do not just write code, but who also understand how data flows through a complex ecosystem. You will be expected to make strategic decisions about storage, compute, and data modeling that balance performance, cost, and reliability.

The questions you will face during the Zillow interview process are designed to evaluate your practical engineering skills, system architecture knowledge, and behavioral alignment. These questions are drawn from real-world interview experiences and are structured to test how you solve problems under pressure.

Coding & Algorithms

These questions evaluate your proficiency in Python, data structures, and your ability to write clean, optimized code. You will often use platforms like HackerRank to complete these challenges.

• Write a program to find the first non-repeating character in a stream of property ID strings.
• Given an array of historical listing prices, write an algorithm to find the maximum profit you could make by buying and selling a property at different times.

To succeed in the Zillow Data Engineer interview, you must demonstrate a balanced mix of software development fundamentals and data-specific expertise. Your interviewers will look for structured thinking, clear communication, and a practical approach to system design.

Role-Related Knowledge – You must show deep proficiency in Python, SQL, and big data technologies like Spark. Interviewers will evaluate your understanding of distributed computing, data storage formats, data modeling, and performance optimization.

Problem-Solving Ability – You will be presented with ambiguous, real-world scenarios, such as designing a pipeline for messy, external real estate feeds. You must be able to break down these complex problems, ask clarifying questions, and propose structured, scalable solutions.

Code Quality & Refactoring – Unlike many companies that only test pure algorithmic coding, Zillow places a strong emphasis on your ability to read, maintain, and optimize existing code. You must demonstrate that you write clean, readable, and modular code that can be easily tested and refactored.

Cultural Alignment – Zillow values collaboration, transparency, and a customer-first mindset. You should be prepared to discuss how you navigate team dynamics, manage project ambiguity, and ensure that your technical solutions deliver real business value.

The interview process for a Data Engineer at Zillow is highly structured and typically takes between three to six weeks to complete. The process is designed to evaluate both your technical depth and your cultural alignment through a series of progressive stages.

The journey begins with an initial recruiter phone screen, which is focused on your background, career goals, and basic alignment with the role. If you move forward, you will complete an initial technical assessment, which often consists of a coding challenge on HackerRank or a live technical screen with a senior engineer focusing on Python, SQL, and fundamental ETL concepts.

For candidates who pass the initial screening, the process culminates in a comprehensive virtual onsite. This stage consists of four to six rounds, covering coding, code refactoring, system design, and behavioral questions. Throughout the process, Zillow interviewers are known to be respectful, collaborative, and highly professional, treating the technical rounds as peer-to-peer working sessions.

The timeline above outlines the standard progression of the Zillow interview loop. It starts with high-level behavioral and resume screening, moves into foundational technical validation, and finishes with a rigorous onsite loop testing deep engineering execution. You should manage your energy by solidifying your coding and ETL fundamentals early before pivoting to system design and behavioral preparation for the final rounds.

To pass the Zillow technical bar, you must perform consistently across several core competency areas. Each round of the onsite interview targets specific skills that are critical for day-to-day success in the role.

Coding & Refactoring

This evaluation area tests your ability to write clean, production-grade Python code. Rather than just focusing on complex dynamic programming algorithms, Zillow frequently evaluates your ability to refactor existing, suboptimal code to improve its readability, maintainability, and execution speed.

Be ready to go over:

• Object-Oriented Programming (OOP) – Structuring your code using classes, inheritance, and clean interfaces.
• Code optimization – Identifying bottlenecks, reducing time complexity, and minimizing memory usage in data processing scripts.
• Testing and edge cases – Writing unit tests and handling null values, malformed inputs, and empty datasets.

Example scenarios:

• "Here is a legacy script that parses and processes property listings. Refactor it to make it modular, testable, and more efficient."
• "Implement a function to clean and deduplicate an incoming stream of geographic coordinates, ensuring optimal memory utilization."

ETL & Big Data Engineering

This area assesses your hands-on experience with distributed computing systems and data pipeline orchestration. You must demonstrate that you understand how to process data efficiently at scale.

Be ready to go over:

• Spark optimization – Managing partitions, avoiding shuffle operations, and utilizing caching and broadcasting effectively.
• Data modeling – Designing star schemas, snowflake schemas, and choosing the right partition keys for data lakes.
• Data quality framework – Building automated checks to validate schema conformance, data completeness, and referential integrity.
• Advanced concepts (less common) – Graph data processing, custom Spark UDF development, and tuning JVM memory parameters.

Example scenarios:

• "Explain how you would debug a Spark job that is failing with an OutOfMemory (OOM) error during a heavy aggregation step."
• "How would you design a data schema to track the historical price changes of every home listed on Zillow over a ten-year period?"

System Design & Data Architecture

This round evaluates your ability to build end-to-end data platforms. You will be expected to make architectural decisions and justify them based on scale, cost, latency, and business requirements.

Be ready to go over:

• Ingestion patterns – Choosing between batch processing and real-time streaming (e.g., Apache Kafka) based on data freshness needs.
• Storage tiering – Selecting the appropriate storage engines (e.g., S3, relational databases, NoSQL, data warehouses) for different access patterns.
• Scalability and fault tolerance – Designing self-healing pipelines that can handle infrastructure failures without data loss.

Example scenarios:

• "Design a system to ingest and process real-time clickstream data to power a 'recommended homes' feature on the Zillow homepage."
• "Architect a scalable data pipeline to import, validate, and merge property records from 500 different regional MLS feeds daily."