Amazon Payment Products team creates and manages a global portfolio of products, including co-branded credit cards, installment financing, third party redemptions, and financial services marketplaces. Within this team, 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 *Design logical and physical data models for complex, multi-grain datasets. Define fact/dimension structures, handle SCDs, and optimize for downstream query patterns. *Implement data ingestion pipelines (real-time and batch) using AWS technologies and big data tools, following best practices in ETL/ELT design. *Evaluate and select appropriate data storage and processing technologies based on access patterns, cost, scalability, and latency trade-offs. *Own data quality frameworks including validation, reconciliation, SLA definition, and certification of production datasets. *Gather business and functional requirements; translate into robust, scalable, operable solutions with flexible and adaptable data architecture. *Collaborate with engineers and scientists to adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation. *Drive continuous improvement of reporting and analysis processes; automate self-service data access and reduce manual dependencies. *Maintain operational excellence of data pipelines including monitoring, alerting, root-cause analysis, and post-incident remediation.