Are you passionate about building business insights that drive decision making at the highest levels? Do you want to be a part of a fast-paced, ambiguous environment and contribute to Amazon Payments charter?
In this role you will be responsible for solving complex and ambiguous data and metric problems. You will use latest AWS provided Data Analytics and GenAI tools (such as Quicksight, Amazon Q, Sagemaker) to provide business metrics for senior leaders. You should have a track record of end-to-end ownership and successfully delivering results in a fast-paced and dynamic business environment. You should be able to work on complex initiatives, deal with ambiguity. You will be required to work with senior leaders, product managers, marketing, finance, operations, program managers and other data teams to understand metrics such as GMS Penetration, new acquisitions, approval rates, deposit rates etc.
Key job responsibilities
As a business intelligence engineer, you will/may:
* Be highly data driven, resourceful, customer-obsessed and team oriented
* Use latest AWS provided data analytics and GenAI tools (such as Quicksight, Amazon Q, Sagemaker)
* Design, develop, and implement scalable, automated processes for data extraction, processing, and evaluation
* Define data driven approaches and metrics strategy to uncover new insights
* Have a passion for sourcing, manipulating, and visualizing data to tell business stories
* Be able to clearly communicate strategic findings and recommendations
* Translate complex, technical concepts into business terms and vice versa
* Work with engineering and data teams to support the development of tools and dashboards
About the team
North America Payment Products (NAPP) Science team within GPTech, Amazon Payments, owns the Data and ML use cases, supporting the NAPP business. This role will be focusing on Private Label Credit Cards (PLCC) suite of products also called Store Card.
