Data Engineer I
About The Position
Our Opportunity: Chewyʼs AI & Data organization is seeking a Data Engineer I to join the Enterprise Data Systems (EDS) team. EDS powers the trusted, governed, and intelligent data foundation that fuels analytics, machine learning, and AI across Chewy. As a Data Engineer I, youʼll build your skills as an apprentice data builder—learning how to design, test, and deliver the data pipelines that make insights and AI possible. Youʼll work closely with experienced engineers to gain deep exposure to Chewyʼs modern data stack, Snowflake, dbt Cloud, Kafka, AWS, and Tableau, while developing your technical and problem-solving foundation. Weʼre looking for teammates who are endlessly curious, passionate about learning, and excited about how data and AI can transform customer and business experiences. What You'll Do: Build and maintain small-scale ingestion and transformation pipelines under mentorship from senior engineers. Write clean, well-documented SQL and Python for data preparation, validation, and automation. Learn and apply dbt Cloud modeling concepts for structured, testable, and auditable data development. Monitor data quality and pipeline performance; assist in triaging and resolving basic issues. Participate in code reviews to understand design principles, version control, and testing best practices. Collaborate with analytics and data science teams to deliver well-structured, reliable datasets. Grow familiarity with Kafka streaming, data observability, and cloud infrastructure fundamentals. Maintain documentation for data catalog and objects you own; contribute basic unit tests for pipeline code. Continuously learn about AI, automation, and how they are reshaping data engineering. What You'll Need: 0-2 years of professional experience in data engineering, analytics engineering, or a related technical field. Proficiency in SQL and working familiarity with Python. Basic understanding of cloud data warehouses (Snowflake, Redshift, or BigQuery preferred). Working familiarity with Terraform so you can read modules and safely make small, reviewed plan/apply changes (variables, remote state) toAWS/Snowflake resources under mentorship. Curiosity about dbt, Kafka, and modern orchestration frameworks. A learning mindset with a passion for AI, data-driven decision-making, and continuous improvement. Clear communication, eagerness to ask questions, and openness to feedback. Why You'll Love This Role: Learn directly from experience data engineers in a collaborative, AI-driven environment. Gain hands-on experience with one of the most modern enterprise data stacks in retail. Contribute to projects that power real business insights and customer experiences. Build a foundation for a long-term career in data, AI, and intelligent systems.
Requirements
- 0-2 years of professional experience in data engineering, analytics engineering, or a related technical field.
- Proficiency in SQL and working familiarity with Python.
- Basic understanding of cloud data warehouses (Snowflake, Redshift, or BigQuery preferred).
- Working familiarity with Terraform so you can read modules and safely make small, reviewed plan/apply changes (variables, remote state) toAWS/Snowflake resources under mentorship.
- Curiosity about dbt, Kafka, and modern orchestration frameworks.
- A learning mindset with a passion for AI, data-driven decision-making, and continuous improvement.
- Clear communication, eagerness to ask questions, and openness to feedback.
Responsibilities
- Build and maintain small-scale ingestion and transformation pipelines under mentorship from senior engineers.
- Write clean, well-documented SQL and Python for data preparation, validation, and automation.
- Learn and apply dbt Cloud modeling concepts for structured, testable, and auditable data development.
- Monitor data quality and pipeline performance; assist in triaging and resolving basic issues.
- Participate in code reviews to understand design principles, version control, and testing best practices.
- Collaborate with analytics and data science teams to deliver well-structured, reliable datasets.
- Grow familiarity with Kafka streaming, data observability, and cloud infrastructure fundamentals.
- Maintain documentation for data catalog and objects you own; contribute basic unit tests for pipeline code.
- Continuously learn about AI, automation, and how they are reshaping data engineering.
Benefits
- Learn directly from experience data engineers in a collaborative, AI-driven environment.
- Gain hands-on experience with one of the most modern enterprise data stacks in retail.
- Contribute to projects that power real business insights and customer experiences.
- Build a foundation for a long-term career in data, AI, and intelligent systems.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
