Amazon Manager Data Engineering Shopbop 

United States, Wisconsin, Madison 

252064256

You will manage a team of three engineers and eight contractors who source, clean, and host Shopbop’s data mart. You will manage the team’s commitments and become a strong thought partner for partners in all departments, as well as a contributor to the strong technical culture at Shopbop and our parent company - Amazon. You will coach a diverse team of data professionals to become experts in their field. You will partner with our Principal Data scientist to curate the data needed to unlock data science and AI opportunities. You will also oversee the migration of our platform from legacy in-house and Informatica systems to modern AWS technology.We are looking for a candidate willing to be in person at a Shopbop location in Madison, WI or New York, NY, but support a team that is remote. On most days you will work remotely arranging your hours to successfully manage a team that spans all US time zones. You will work in-office weekly to partner face to face with peers and stakeholders. You will have latitude to set your schedule and location to make this work - we support diverse work arrangements. Anticipate quarterly travel to Madison, WI, New York, NY, or Seattle, WA.
Key job responsibilities
A day in the life

- Experience managing a data or BI team
- Experience communicating to senior management and customers verbally and in writing
- Experience leading and influencing the data or BI strategy of your team or organization
- Experience hiring, developing and promoting engineering talent
- 3+ years of data engineering experience
- 2+ years of developing and operating large-scale data structures for business intelligence analytics (using ETL/ELT processes) experience
- 2+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience

- Experience with AWS Tools and Technologies (Redshift, S3, EC2)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR