Amazon’s Consumer Payments organization is seeking a highly quantitative, experienced Data Engineer to drive growth through analytics, automation of data pipelines, and enhancement of self-serve experiences. . You will succeed in this role if you are an organized self-starter who can learn new technologies quickly and excel in a fast-paced environment. In this position, you will be a key contributor and sparring partner, developing analytics and insights that global executive management teams and business leaders will use to define global strategies and deep dive businesses.
You will be part the team that is focused on acquiring new merchants from around the world to payments around the world. The position is based in India but will interact with global leaders and teams in Europe, Japan, US, and other regions. You should be highly analytical, resourceful, customer focused, team oriented, and have an ability to work independently under time constraints to meet deadlines. You will be comfortable thinking big and diving deep. A proven track record in taking on end-to-end ownership and successfully delivering results in a fast-paced, dynamic business environment is strongly preferred.
Responsibilities include but not limited to:
- Design, develop, implement, test, and operate large-scale, high-volume, high-performance data structures for analytics and Reporting.
- Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL, AWS – Redshift, and OLAP technologies, Model data and metadata for ad hoc and pre-built reporting.
- Work with product tech teams and build robust and scalable data integration (ETL) pipelines using SQL, Python and Spark.
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.
- Interface with business customers, gathering requirements and delivering complete reporting solutions.
- Collaborate with Analysts, Business Intelligence Engineers and Product Managers to implement algorithms that exploit rich data sets for statistical analysis, and machine learning.
- Participate in strategic & tactical planning discussions, including annual budget processes.
- Communicate effectively with product/business/tech-teams/other Data teams.