Senior Data Engineer, Shopbop

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience operating large data warehouses
- Master's degree
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions

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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $139,100/year in our lowest geographic market up to $240,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
As the Senior DE on Shopbop’s Data Engineering team you will lead stragecic-decision making for the team, drive adoption of best practices, and routinely solve complex data engineering questions. Your work will unblock and accelerate critical data projects that drive Shopbop’s mission to be “the daily destination for style inspiration and discovery” - working on projects that drive outcomes from optimizing marketing investment, to lowering delivery speed to customers. You will set the technical direction for our Data Engineering team as they work to migrate from a legacy Informatica system to a modern data architecture and raise the bar on the insights Shopbop can drive with data. You will partner closely with junior data engineers, peer software engineers, and leadership to define the technical direction for Shopbop's data systems, coach colleagues on best practices for data ownership, and advise peers at Shopbop and Amazon (our parent company) on technical topics. You will define and teach others about the best practices for data ownership and the ownership boundaries between source teams and DE teams. Your work will improve the quality of data and insights at Shopbop, driving important customer outcomes from purchasing the right products that spark customer’s style obsessions, lowering delivery speed through shipping insights, or powering science-driven algorithms.

The Shopbop Data Engineering team has 3 engineers and 8 contractors who source, clean, and host Shopbop's data mart. You will be a senior advisor to the team providing your expertise at standups, code reviews, and design reviews. You will represent the team in technical meetings. You will also act as an auditor and contributor to technical discussions that cross the Amazon Fashion & Fitness organization partnering closely with Senior Engineers, BIEs, Principal Engineers and Principal Data Scientists. You will have access to a coaching and mentoring program that provides access to experts across Amazon. We are looking for a candidate willing to be in person at a Shopbop location in New York, NY weekly, but working virtually most of the time with partners across all US Timezones. Expect 1-2/year travel to Madison WI, New York NY, or Seattle WA.


Key job responsibilities
You will be the key decision maker for the architecture of Shopbop’s ETL processes, making sure we produce reliable, accurate, timely data for our customers. You will be the leader in deciding what patterns, practices, and approaches the DE team uses to develop their systems. You will drive the quality bar on the team by inventing new components and processes, using code reviews to drive best practices, and advocating for time to invest in the right Operational Excellence investments for the team. You will coach outside the team as well, advising peer Software teams to provide accurate data. You will act as an advisor to Shopbop Technology at org-wide operations reviews, in technical deep dives, and in review project plans. You will bring data expertise to other groups, but also act as a general expert.

A day in the life
You start the day reviewing slack: you respond to an email asking for an invite to the design review for a new Marketing project to make sure their data plan is sound. You work for several hours on a migration project, building a component to help simplify this (and future) integrations. At daily standup, you notice one of the engineers is having trouble optimizing their ETL flow, so you call them afterwards - unblocking their work. In the afternoon you complete a code review, the work looks good but needs a few additional alarms. You finish the day at the Shopbop operations review, where you share good news with your peers: nightly runs are completing 20% faster due to optimizations you made.

About the team