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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer II, Canada Product & Tech - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $132,100.0 - $178,800.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Code Review, Databases, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Stores, Data Systems, Data Warehousing, Graph Database, Identity and Access Management, Query Optimization, DataOps, Software Engineering, SQL Databases, IMR (Goal Tracking System), Technical Debt, Electronic Medical Records, Generative AI, Data Layers, AWS Glue, Non-relational Database, Software Version Control, Data Pipelines, Amazon Redshift - **Published:** August 5, 2026 - **Apply:** https://www.amazon.jobs/en/jobs/10489174/data-engineer-ii-canada-product-tech ## About the Role 3+ years of data engineering experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience - Experience with data modeling, warehousing and building ETL pipelines - Bachelor's degree, Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) - Bachelor's degree or above in Science, Technology, Engineering, or Mathematics (STEM), or experience in defining and creating benchmarks for assessing GenAI model performance - Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences. ## Description 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. About the team 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 ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Got AI ideas but no money? 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