What is a Data Engineer at Chewy?

As a Data Engineer at Chewy, you are the foundational builder of the data ecosystem that powers one of the fastest-growing e-commerce platforms in the world. Chewy relies on massive volumes of data to drive everything from personalized pet profiles and Autoship recommendations to complex supply chain logistics and fulfillment center operations. In this role, you are not just moving data; you are engineering the high-performance pipelines that enable real-time decision-making across the entire enterprise.

The impact of this position is vast. Your work directly influences the customer experience by ensuring that data science models have clean, reliable data to predict what pet parents need before they even know they need it. You will collaborate with product managers, data scientists, and software engineers to design scalable architectures that handle millions of daily transactions. Whether you are optimizing a complex Snowflake query or building robust streaming pipelines in AWS, your engineering decisions dictate the speed and accuracy of Chewy’s business intelligence.

Expect a fast-paced, highly collaborative environment where scale and complexity are the norm. Chewy operates at a massive scale, meaning you will face unique distributed computing challenges. This role is perfect for builders who are passionate about data quality, thrive in a culture of ownership, and want to see their technical solutions directly improve the lives of pets and pet parents everywhere.

Getting Ready for Your Interviews

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.

Interview Process Overview

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.

Deep Dive into Evaluation Areas

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."

Key Responsibilities

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.

Role Requirements & Qualifications

To be a competitive candidate for a Data Engineer at Chewy, you need a strong blend of software engineering principles and data warehousing expertise. Expectations scale with the level you are targeting; a Level I role focuses heavily on execution and coding, while Level II and III roles require deep architectural knowledge and project leadership.

• Must-have skills – Advanced proficiency in SQL and Python. Deep experience with cloud data warehousing (Snowflake, AWS Redshift, or Google BigQuery). Hands-on experience with distributed data processing frameworks, specifically Apache Spark or PySpark. Strong knowledge of data pipeline orchestration tools like Apache Airflow.
• Experience level – For a Data Engineer I, 1-3 years of relevant experience is typical. A Data Engineer II generally requires 3-5 years of experience building complex pipelines in a production environment. A Data Engineer III requires 5+ years of experience, including proven success in system design, leading large-scale data migrations, and mentoring teams.
• Soft skills – Exceptional cross-functional communication skills. You must be able to translate complex technical constraints into business impacts for non-technical stakeholders. A strong sense of ownership and a proactive approach to problem-solving are essential.
• Nice-to-have skills – Experience with streaming technologies like Apache Kafka or AWS Kinesis. Familiarity with CI/CD practices for data engineering (e.g., dbt, Terraform). Prior experience in the e-commerce or retail logistics sector is a distinct advantage, as it shortens the domain knowledge learning curve.

Common Interview Questions

The questions below are representative of what candidates face during the Chewy interview process. They are designed to illustrate the patterns and themes you will encounter, rather than serving as a strict memorization list. Prepare to adapt your knowledge to similar scenarios.

SQL and Database Concepts

This category tests your ability to write complex queries, understand execution plans, and design efficient data models for an e-commerce environment.

• Write a query to find the 30-day rolling average of daily sales per product category.
• How do you optimize a query that is scanning a massive fact table but running too slowly?
• Explain the difference between a star schema and a snowflake schema. Which would you choose for Chewy's order history and why?
• How do you handle duplicate records in a dataset using SQL window functions?
• Design a relational schema to track customer support tickets, agents, and resolution times.

Coding and Data Processing

These questions evaluate your proficiency in Python and your ability to manipulate data programmatically, often focusing on distributed processing concepts.

• Write a Python script to merge two large CSV files based on a common key, handling missing values appropriately.
• How does Spark handle data shuffling, and how can you minimize it to improve job performance?
• Given an array of integers, write a function to return the top K frequent elements.
• Explain the difference between map and flatMap in Spark.
• How would you structure a Python application to pull data from a paginated REST API and save it to an AWS S3 bucket?

System Architecture and Pipelines

This area tests your ability to design end-to-end systems, make architectural trade-offs, and ensure data reliability at scale.

• Design a data pipeline to ingest 10 million daily clickstream events, process them, and make them queryable within 5 minutes.
• How would you monitor an Airflow pipeline to ensure data quality and alert the team if data is missing or anomalous?
• Walk me through the architecture of a data lake vs. a data warehouse. How do they complement each other?
• What strategies would you use to backfill a year's worth of historical data into a newly created pipeline without impacting production?
• Compare the benefits of an ETL approach versus an ELT approach in a modern cloud environment.

Behavioral and Operating Principles

These questions assess your cultural fit, leadership qualities, and how you embody Chewy's core values.

• Tell me about a time you had to deliver a critical project under a very tight deadline. How did you prioritize?
• Describe a situation where you disagreed with a senior engineer's architectural design. How did you handle it?
• Give an example of a time you identified a process improvement that saved the team time or money.
• Tell me about a time you failed or made a significant mistake in production. What did you learn?
• How do you ensure you are building data products that truly put the "Customer First"?

Frequently Asked Questions

Q: How difficult is the technical screen, and how much should I prepare? The technical screen is moderately difficult and highly practical. You should expect live coding in both SQL and Python. Spend at least a week refreshing your advanced SQL (window functions, CTEs) and practicing data manipulation tasks in Python. Speed and clean syntax are important here.

Q: What differentiates a successful candidate from an average one? Successful candidates at Chewy don't just write code that works; they explain why it works and how it scales. A standout candidate will proactively discuss edge cases (e.g., "What if this API rate limits us? What if the data volume doubles on Black Friday?") and tie their technical decisions back to business value.

Q: What is the culture like for Data Engineers at Chewy? The culture is fast-paced, highly collaborative, and deeply data-driven. Because Chewy operates at a massive scale with millions of active customers and complex logistics, the engineering culture values ownership, operational excellence, and a bias for action. You will be expected to own your pipelines end-to-end.

Q: How long does the interview process typically take? From the initial recruiter screen to the final offer, the process usually takes between 3 to 5 weeks. The timeline can vary slightly depending on interviewer availability and whether you are interviewing for the Boston, MA or Plantation, FL offices.

Q: Are the roles fully remote, hybrid, or onsite? Chewy generally operates on a hybrid model for its major tech hubs in Boston, MA and Plantation, FL. You should be prepared to discuss your location preferences and willingness to work in a hybrid environment during your initial recruiter screen.

Other General Tips

• Master the STAR Method: Chewy heavily values structured behavioral answers. For every project on your resume, prepare a Situation, Task, Action, and Result. Focus heavily on the "Action" (what you specifically did) and the "Result" (quantifiable metrics like "reduced query time by 40%").
• Clarify Before Coding: Whether in SQL or Python, never start typing immediately. Take two minutes to ask clarifying questions about the data schema, expected data volumes, and edge cases. This demonstrates maturity and architectural thinking.

• Design for E-commerce Scale: When given a system design prompt, anchor your answer in reality. Mention phenomena like "Black Friday traffic spikes" or "inventory sync delays." Showing that you understand the business context of e-commerce will set you apart.
• Know Your Cloud Limits: If you propose a solution using AWS Lambda or Spark, be prepared to discuss their limitations. Knowing when not to use a technology is a strong signal of a senior engineer.

• Embrace the Pet Culture: Chewy is passionate about pets. While you don't need to own a pet to work there, showing enthusiasm for the company's mission to be the most trusted and convenient destination for pet parents is a great way to build rapport with your interviewers.

Summary & Next Steps

Interviewing for a Data Engineer position at Chewy is an exciting opportunity to join a high-performing team at the intersection of massive e-commerce scale and advanced data architecture. You will be challenged to demonstrate not only your coding and design prowess but also your ability to take ownership of complex, business-critical pipelines. The work you do here directly impacts millions of customers and their pets, making it a deeply rewarding environment for builders.

This compensation data provides a baseline expectation for the role. Keep in mind that total compensation at Chewy typically includes a base salary, an annual performance bonus, and equity (RSUs), which scales significantly as you move from Level I to Level III. Use this information to anchor your expectations and have informed conversations with your recruiter when the time comes.

To succeed, focus your preparation on mastering advanced SQL, distributed data processing in Python/Spark, and scalable cloud architectures. Just as importantly, reflect on your past experiences and frame them through the lens of Chewy’s Operating Principles. You have the skills and the drive to excel in this process. Continue to practice your technical communication, explore additional resources on Dataford to refine your system design frameworks, and approach your interviews with confidence. You are ready to build the future of pet e-commerce!