Amazon Consumer Gift Cards is a fast-growing, multi-billion-dollar, worldwide business with a mission to provide the world’s most desired and convenient gifting option to customers. Our team enables decision making for WW Gift Cards team leveraging Data Science, Machine Learning, Analytics and Data Engineering. We are looking for a Data Engineer (DE) to play a significant role in building large-scale, high-volume, high-performance data integration and delivery services. These data solutions would be primarily used in periodic reporting, and drive business decision making while dealing efficiently with the massive scale of data available through our Data Warehouse as well as our software systems. You will be responsible for designing and implementing solutions using third-party and in-house reporting tools, modeling metadata, building reports and dashboards, and administering the platform software. You are expected to build efficient, flexible, extensible, and scalable data models, ETL designs and data integration services. You are required to support and manage growth of these data solutions. You are passionate about working with huge datasets and have experience with the organization and curation of data for analytics. You have a strategic and long term view on architecting advanced data eco systems. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams.


Key job responsibilities
In this role, you have the opportunity to:
- Implement data ingestion routines both real time and batch using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.
- Design, implement and operate large-scale, high-volume, high-performance data for analysis and data science.
- Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.
- Collaborate with engineers/scientists to help adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation
- Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service modeling and production support for customers.