Preparing for a Data Engineer interview at Chewy requires a balanced focus on core computer science fundamentals, specialized data architecture skills, and a deep alignment with the company's operating principles.

You will be evaluated across several key dimensions:

• Technical and Domain Expertise – This evaluates your proficiency in Python, SQL, distributed data processing (like Spark), and cloud data warehousing (like Snowflake and AWS). Interviewers look for your ability to write clean, optimized code and your understanding of data modeling principles.
• System Design and Architecture – This assesses how you structure complex data ecosystems. You will need to demonstrate your ability to design scalable, fault-tolerant batch and streaming pipelines that can handle Chewy's massive e-commerce data volume.
• Problem-Solving Ability – This measures how you approach ambiguous, open-ended challenges. Interviewers want to see you break down complex business requirements into logical engineering steps, identifying edge cases and performance bottlenecks along the way.
• Culture and Operating Principles – Chewy places a heavy emphasis on its core values, such as "Customer First," "Think Big," and "Deliver Results." You will be evaluated on your ability to communicate effectively, take ownership of your projects, and navigate a fast-moving, cross-functional environment.

The interview process for a Data Engineer at Chewy is rigorous, structured, and designed to assess both your technical depth and your cultural alignment. Typically, the process begins with an initial recruiter phone screen to discuss your background, location preferences (such as the Boston, MA or Plantation, FL hubs), and the specific level you are targeting (Data Engineer I, II, or III). This is followed by a technical phone screen, usually conducted via a shared coding environment, where you will face a mix of SQL and Python coding challenges alongside basic data concepts.

If you pass the technical screen, you will advance to the virtual onsite loop. This final stage generally consists of four to five distinct rounds. You can expect a deep dive into data modeling, a system design and architecture round, an advanced coding session (often focusing on data structures or data manipulation), and a dedicated behavioral round focused on Chewy’s Operating Principles. The pace is intensive but collaborative; interviewers at Chewy want to see how you work through problems in real-time and how you respond to feedback.

What makes this process distinctive is the heavy emphasis on real-world e-commerce scenarios. You will rarely be asked purely academic brain-teasers; instead, you will be asked to design pipelines for inventory management, optimize queries for customer order history, or troubleshoot a failing daily ETL job.

This visual timeline outlines the typical progression from the initial recruiter screen through the final virtual onsite loop. Use this to pace your preparation, focusing first on core SQL and Python for the technical screen, and then expanding into system design and behavioral storytelling for the onsite stages. Keep in mind that expectations for system design and architectural leadership will scale up significantly if you are interviewing for a Data Engineer III position compared to a Level I role.

SQL and Data Modeling

• SQL is the bedrock of data engineering at Chewy. You will be evaluated on your ability to write complex, highly optimized queries that can process millions of rows efficiently. Interviewers look for a strong grasp of window functions, complex joins, aggregations, and query execution plans.
• Data modeling is equally critical. You must understand the trade-offs between different modeling paradigms (like Kimball dimensional modeling vs. Data Vault) and know how to design schemas that balance read performance with storage costs.
• Strong performance in this area means not just writing a query that works, but writing one that is readable, scalable, and optimized for a columnar database like Snowflake.

Be ready to go over:

• Advanced SQL functions – Window functions (RANK, DENSE_RANK, ROW_NUMBER), CTEs, and recursive queries.
• Dimensional Modeling – Designing fact and dimension tables, handling slowly changing dimensions (SCDs).
• Performance Tuning – Understanding execution plans, indexing strategies, and partition pruning.
• Advanced concepts (less common) – Specific Snowflake optimization techniques like clustering keys and micro-partitions.

Example questions or scenarios:

• "Design a data model for Chewy's Autoship program, including tables for customers, products, and subscription schedules."
• "Write a SQL query to find the top 3 best-selling pet food brands in each state over the last 30 days, using window functions."
• "How would you handle a slowly changing dimension for a customer's shipping address that changes frequently?"

Python and Data Processing

• You need strong programming skills to build and maintain robust ETL/ELT pipelines. Python is the primary language evaluated, often in conjunction with PySpark for distributed processing.
• You will be tested on your ability to manipulate data frames, handle missing or malformed data, and write clean, modular code.
• Strong candidates will demonstrate an understanding of time and space complexity, and how to apply basic data structures (like dictionaries and lists) to solve data-centric problems.

Be ready to go over:

• Core Python – Data structures, list comprehensions, error handling, and object-oriented principles.
• Data manipulation – Using pandas or PySpark to filter, aggregate, and transform large datasets.
• Distributed computing – Understanding Spark architecture, RDDs vs. DataFrames, shuffling, and partitioning.
• Advanced concepts (less common) – Managing memory out-of-bounds errors in Spark and optimizing broadcast joins.

Example questions or scenarios:

• "Write a Python function to parse a messy JSON log file of customer website clicks and extract specific session IDs."
• "Explain how a Spark join works under the hood and how you would optimize a join between a massive orders table and a small regions table."
• "Given a list of dictionaries representing pet profiles, write a script to deduplicate the records based on pet name and owner ID."

Cloud Architecture and System Design

• As a Data Engineer, especially at the II and III levels, you must understand how to piece together various cloud services to build end-to-end pipelines. Chewy relies heavily on AWS.
• You will be evaluated on your ability to design fault-tolerant, scalable systems. This includes choosing the right storage solutions, compute engines, and orchestration tools.
• A strong performance involves driving the design conversation, asking clarifying questions about data volume and latency requirements, and explicitly discussing the trade-offs of your architectural choices.

Be ready to go over:

• AWS Ecosystem – S3, EC2, EMR, Lambda, Redshift, and IAM roles.
• Orchestration – Using Apache Airflow to schedule and monitor complex DAGs.
• Streaming vs. Batch – Knowing when to use Kafka/Kinesis for real-time data versus nightly batch jobs.
• Advanced concepts (less common) – Designing idempotency into data pipelines and implementing data quality frameworks.

Example questions or scenarios:

• "Design an end-to-end data pipeline that ingests real-time inventory updates from our fulfillment centers and makes them available for a dashboard."
• "How would you design a system to ensure that a daily ETL job in Airflow is idempotent and handles upstream failures gracefully?"
• "Compare the use cases for Amazon S3, Redshift, and a transactional database like PostgreSQL."

Behavioral and Operating Principles

• Chewy is highly mission-driven, and behavioral fit is heavily weighted. Interviewers will look for evidence of how you have operated in previous roles using the STAR method (Situation, Task, Action, Result).
• You are evaluated on your communication, your ability to handle conflict, and your bias for action.
• Strong candidates weave Chewy’s Operating Principles (like "Customer First" and "Deliver Results") naturally into their past experiences, highlighting metrics and tangible outcomes.

Be ready to go over:

• Ownership – Times you took the initiative to fix a broken process or pipeline without being asked.
• Handling ambiguity – Navigating projects with unclear requirements or shifting deadlines.
• Cross-functional collaboration – Working with data scientists or product managers to define data needs.

Example questions or scenarios:

• "Tell me about a time you had to push back on a product manager because their data request was not feasible within the given timeline."
• "Describe a situation where you identified a major data quality issue. How did you resolve it and prevent it from happening again?"
• "Give an example of a time you had to learn a completely new technology on the fly to deliver a project."

As a Data Engineer at Chewy, your day-to-day work revolves around building, optimizing, and maintaining the data infrastructure that supports business intelligence, analytics, and machine learning. You will be responsible for designing and developing scalable ETL and ELT pipelines that move vast amounts of structured and unstructured data from transactional databases into central data warehouses like Snowflake.

Collaboration is a massive part of the role. You will work closely with Software Engineering teams to ensure that application data is emitted correctly, and with Data Science teams to ensure they have the clean, reliable features needed to train their models. You will frequently participate in architecture review sessions, code reviews, and sprint planning, ensuring that data best practices are baked into the development lifecycle from day one.

You will also spend a significant portion of your time on performance tuning and operational excellence. This means monitoring Airflow DAGs, troubleshooting failed jobs, optimizing long-running SQL queries, and implementing data quality checks to catch anomalies before they impact downstream reporting. As you progress to a Data Engineer II or III, you will take on more project leadership, mentoring junior engineers and driving the technical vision for your team's data architecture.