Description

Amazon.com seeks a Business Intelligence Engineer to be a key partner for an exciting new initiative-Gen AI within Amazon’s Advertising business, a high-growth business with a range of product offerings for advertisers. We are looking for a top analytical mind capable of understanding our complex ecosystem of Advertising Creative Gen AI business.

As a key BI partner, the role will have the opportunity to use one of the world's largest eCommerce and advertising data sets to influence the long-term evolution of our Gen AI products. This role requires an individual with excellent business, communication, and technical skills, enabling collaboration with various functions, including product managers, software engineers, economists and data scientists, as well as senior leadership. This role will create and enhance Business Reviews decks and performance monitoring reports with automated data pipelines to find insights that product and business team should focus on.

The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must.

This role will influence the direction of the Gen AI business by leveraging our data to deliver insights that drive decisions and actions. The role will involve translating broad business problems into specific analytics projects, conducting deep quantitative analyses, and communicating results effectively. We see a high potential for growth in this role as we transform our data into actionable insights to continue to fuel the growth of this business. The role will help the organization identify, evaluate, and evangelize new techniques and tools to continue to improve our ability to deliver value to Amazon’s customers.

Key job responsibilities
1. Metrics development through Weekly Business Reviews decks and performance monitoring reports (such as Quicksight, Excel)
2. Deep Dive Analysis and data insights (such as SQL, Python)
3. Data infrastructure and automation (such as ETL data pipelines using datanet, Cradle, Redshift, S3, Athena)