Data Engineer II, Canada Product & Tech

The CA Retail Analytics team powers data-driven decision-making for one of Amazon's fastest-growing retail organizations. We support critical business functions across 3P, Prime, Marketing, Finance, Cross Border Product, North America Language Experience, Supply Chain Excellence , Canada Customer and Seller Experience, Delivery Speed and Experience, and other emerging CA Stores initiatives. As CA's footprint expands, we're building the foundational data infrastructure to enable self-service analytics, GenAI integration, and proactive insights at scale. We're seeking a Data Engineer who thrives on building scalable, reliable data systems that unlock business value. You are expected to architect and build large-scale, high-performance data integration and data models that power business-critical analytics across CA Stores. You'll design and implement robust data solutions that handle massive data volumes from our Data Warehouse and distributed software systems, enabling reporting, dashboards, and strategic decision-making for stakeholders across the organization. This is a foundational role where you'll transform CA's analytics from reactive, fragmented solutions into a systematic, scalable data architecture that serves as the backbone for current and future business needs. Key job responsibilities What You'll Do Build Scalable Data Infrastructure • Design and implement robust ETL/ELT pipelines using AWS technologies (Redshift, S3, Glue, EMR, Lambda) to consolidate and normalize data across CA program footprints • Lead technical strategy with CA Tech’s Software Development team on upcoming product launches • Architect dimensional data models and semantic layers that enable self-service analytics and GenAI tools • Develop automated data quality frameworks with monitoring, alerting, and anomaly detection to ensure data reliability Drive Operational Excellence • Optimize cluster performance and reduce IMR costs through systematic node assessment, query tuning, and resource management • Eliminate technical debt by building centralized data models and eliminating duplicate pipelines, and implementing lifecycle management • Establish data engineering best practices including version control, code reviews, testing frameworks, and comprehensive documentation • Mentor team members on data engineering principles and foster a culture of engineering excellence Enable Innovation & Self-Service • Build AI-ready datasets with well-curated metadata and NLP-friendly schemas to support GenAI initiatives and conversational analytics • Partner with BIEs, Data Scientists, and Product teams to deliver production-grade datasets that power strategic insights • Create repeatable, extensible data products and frameworks that scale across multiple CA business domains Above all you should be passionate about working with huge data sets and someone who loves to bring datasets together to answer business questions and drive change.

Why This Role Matters? Amazon Canada Stores is scaling quickly, and current data solutions were not built for the level of cross-domain complexity we are now operating in. The impact will be visible in how quickly leaders can access consistent metrics, how efficiently teams build on shared datasets, and how sustainably we scale new initiatives. What Success Looks Like • First 90 days: Audit current infrastructure, identify quick wins for cost optimization, and establish DE best practices • 6 months: Implement monitoring frameworks, extensible frameworks and deliver first consolidated data models • 12 – 18 months: Enable self-service analytics capabilities, reduce IMR costs by X%+, and lay groundwork for GenAI integration; deliver AI-ready semantic layers, establish automated data quality systems, and position CA as a leader in analytics innovation