Senior Data Engineer

This posting is for a contract assignment through Amazon’s approved third-party provider; selected candidates will not be employees of Amazon.

About the role

As a Data Engineer on the Shopbop team, you will routinely solve complex data problems, unblocking critical projects that drive Shopbop's mission to be the daily destination for style inspiration and discovery. You will partner closely with stakeholders across the business and the Data Engineering team to deliver new features or migrate legacy features to a modern AWS-based platform. You will also engage with the broader Amazon data engineering community, participating in learning series and operational reviews with industry-leading engineers.

Shopbop is part of Amazon Fashion but maintains a unique vibe and mission to serve fashion-oriented customers. The data engineering team frequently collaborates with teams across the business, offering opportunities to learn about the fashion industry, e-commerce, and employee discounts.

This role requires in-person work at the New York office (JFK94).

Responsibilities

• Own projects that build new data pipelines, modernize existing ones onto the AWS-based platform, or refine pipelines to deliver more value for customers.
• Partner with colleagues across Shopbop to understand their business domains and build solutions that meet their needs, including collaborating with the Science team to bring AI solutions to production.
• Drive improvements to the team's operational health and reduce errors for customers.
• Participate in Amazon's engineering culture, learning from and teaching alongside industry leaders while leveraging AI-powered tools to enhance productivity.
• Architect, design, and implement next-generation data pipelines and BI solutions built on AWS.
• Build and optimize ETL processes to improve data quality, reliability, and freshness.
• Use AI-powered developer and data tools to boost productivity and code quality.
• Translate business stakeholder needs into scalable data solutions.
• Collaborate with the Science team to build data foundations for AI solutions and bring them into production.
• Improve operational excellence through monitoring, automation, and error reduction.