Data Engineer II, Shopbop
Amazon.com
As a Data Engineer at 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”. Wether you are optimizing analysis for marketing to tune their strategies, building new pipelines to drive down shipping speeds, or helping accounting drill down to track our business - your work will empower meaningful change for your customers. You will partner closely with stakeholders across our business and partners on the Data Engineering team to deliver new features, or pitch in to migrate legacy features to a modern AWS-based platform. You will be an active partner in Amazon’s (Shopbop’s parent company) data engineering group, taking part in learning series and operational reviews with industry-leading engineers.
Shopbop is part of Amazon’s Fashion and Fitness team, but with a unique vibe and mission to service fashion-oriented customers. The data engineering team frequently collaborates with all parts of the business, providing opportunities to learn about the Fashion industry and E-Commerce (and employee discounts). We are looking for a candidate willing to be in person at a Shopbop location in New York, or Madison, WI weekly, but working virtually other days with colleagues across all US Timezones. Expect 1/year travel to Madison WI, New York NY, or Seattle WA.
Key job responsibilities
You will own projects that build new data pipelines, migrate pipelines form our legacy Informatica system to a modern AWS-based system, or refine existing pipelines to deliver more value for our customers. You will partner with colleagues across Shopbop to understand their business domains and build solutions that meet their needs. You will also be an active participant within the data team at Shopbop, suggesting improvements that improve the teams operational health, and reduce errors for customers. You will participate in Amazon’s active engineering culture learning and teaching the best.
A day in the life
You start the day responding to an email, and start a brief chat to align on requirements with a stakeholder. You hop into a code review, accepting most of the feedback, but noting an area to discuss. You go heads down for a while working on your current story. After standup, you help a peer struggling with a dataset you know, and discuss their CR feedback. In the afternoon you attend a presentation on how to implement effective data models for Machine Learning, spurring ideas for how to improve Shopbop’s personalization data feed. You attend the weekly operational review, and are pleased to see a fix you made has reduced total run time on a critical job by 40%. You finish up the day by working on your primary deliverable before signing off.
About the team
Shopbop is part of Amazon’s Fashion and Fitness team, but with a unique vibe and mission to service fashion-oriented customers. The data engineering team frequently collaborates with all parts of the business, providing opportunities to learn about the Fashion industry and E-Commerce (and employee discounts). We are looking for a candidate willing to be in person at a Shopbop location in New York, or Madison, WI weekly, but working virtually other days with colleagues across all US Timezones. Expect 1/year travel to Madison WI, New York NY, or Seattle WA.
Key job responsibilities
You will own projects that build new data pipelines, migrate pipelines form our legacy Informatica system to a modern AWS-based system, or refine existing pipelines to deliver more value for our customers. You will partner with colleagues across Shopbop to understand their business domains and build solutions that meet their needs. You will also be an active participant within the data team at Shopbop, suggesting improvements that improve the teams operational health, and reduce errors for customers. You will participate in Amazon’s active engineering culture learning and teaching the best.
A day in the life
You start the day responding to an email, and start a brief chat to align on requirements with a stakeholder. You hop into a code review, accepting most of the feedback, but noting an area to discuss. You go heads down for a while working on your current story. After standup, you help a peer struggling with a dataset you know, and discuss their CR feedback. In the afternoon you attend a presentation on how to implement effective data models for Machine Learning, spurring ideas for how to improve Shopbop’s personalization data feed. You attend the weekly operational review, and are pleased to see a fix you made has reduced total run time on a critical job by 40%. You finish up the day by working on your primary deliverable before signing off.
About the team
Confirm your E-mail: Send Email
All Jobs from Amazon.com