Senior Data Engineer, WWFBA Analytics & Engineering
Amazon.com
World Wide Fulfillment by Amazon (FBA) enables sellers to outsource order fulfillment to Amazon, leveraging our world-class supply chain and technology to provide customers with the fastest delivery promises and seamless returns. Sellers gain access to Prime and non-Prime customers globally while focusing on their core business.
The WWFBA Analytics & Engineering team, part of the FBA Insights & Planning organization, is dedicated to building centralized analytics and data engineering infrastructure for FBA business. Our mission is to deliver scalable data solutions that unify sources of truth, bridge gaps, and reduce operational complexity. We drive incremental improvements while advancing towards long-term strategic goals.
We are seeking an exceptional Senior Data Engineer who is data-driven, detail-oriented, and passionate about solving complex problems. You will collaborate with a diverse team of software engineers, data engineers, and analysts to build a truly global data lake and infrastructure using advanced technologies, including Spark, Ray, DBT, and AWS technologies to enhance performance and efficiency.
As a key member of our team, you will:
- Lead the design and implementation of scalable data pipelines and storage solutions.
- Partner with cross-functional teams, including product managers, program managers, business intelligence engineers (BIEs), and business leaders.
- Drive technical innovation and best practices for data engineering and analytics.
- Develop an AI-powered insights platform to enhance the accuracy and speed of insights generation for the FBA business.
- Collaborate with regional FBA, Finance, and Retail teams to ensure seamless data integration and consistency.
This role requires exceptional analytical thinking, problem-solving skills, and a demonstrated ability to work through ambiguity. You’ll have the opportunity to influence decision-making across WWFBA by providing reliable, actionable insights.
If you're eager to work with large-scale global datasets, explore new technologies, and contribute to the future of FBA, we’d love to hear from you. Reach out to schedule an informational chat with the hiring manager.
The WWFBA Analytics & Engineering team, part of the FBA Insights & Planning organization, is dedicated to building centralized analytics and data engineering infrastructure for FBA business. Our mission is to deliver scalable data solutions that unify sources of truth, bridge gaps, and reduce operational complexity. We drive incremental improvements while advancing towards long-term strategic goals.
We are seeking an exceptional Senior Data Engineer who is data-driven, detail-oriented, and passionate about solving complex problems. You will collaborate with a diverse team of software engineers, data engineers, and analysts to build a truly global data lake and infrastructure using advanced technologies, including Spark, Ray, DBT, and AWS technologies to enhance performance and efficiency.
As a key member of our team, you will:
- Lead the design and implementation of scalable data pipelines and storage solutions.
- Partner with cross-functional teams, including product managers, program managers, business intelligence engineers (BIEs), and business leaders.
- Drive technical innovation and best practices for data engineering and analytics.
- Develop an AI-powered insights platform to enhance the accuracy and speed of insights generation for the FBA business.
- Collaborate with regional FBA, Finance, and Retail teams to ensure seamless data integration and consistency.
This role requires exceptional analytical thinking, problem-solving skills, and a demonstrated ability to work through ambiguity. You’ll have the opportunity to influence decision-making across WWFBA by providing reliable, actionable insights.
If you're eager to work with large-scale global datasets, explore new technologies, and contribute to the future of FBA, we’d love to hear from you. Reach out to schedule an informational chat with the hiring manager.
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