Data Engineer, Amazon Prime Mid
Posted
Summary
Data engineer on Amazon Prime's Member Experience R&D team in Vancouver, building and maintaining terabyte-scale ETL, streaming, and ML data pipelines that power Prime's personalization, targeting, and automated marketing systems. Core stack is AWS-centric: Glue, EMR/Spark, Redshift, S3, Kinesis, Lambda, and DynamoDB.
Amazon Prime's Member Experience organization leads Research & Development for Amazon Prime. The program serves over 300 million members across 25 countries and is key to Amazon's customer growth and engagement. We build algorithms and systems for automated marketing, personalization, targeting, and decisioning using Artificial Intelligence and Economics.
Skills, with evidence
Spark Advance data engineering infrastructure using tools such as AWS Glue, Spark, Amazon EMR, and EMR Serverless to support science and analytics workloads must have Warehousing Experience with data modeling, warehousing and building ETL pipelines must have AWS Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions must have · not practised here Streaming You will work with AWS technologies such as Amazon EMR, Amazon S3, AWS Glue, AWS Lambda, and Amazon DynamoDB to process terabyte-scale data and support real-time access and orchestration across multiple systems. Machine learning Build and maintain big data pipelines for machine learning applications at Amazon scale, ensuring stability and performance across terabyte-sized datasets not practised here Airflow / orchestration You will work with AWS technologies such as Amazon EMR, Amazon S3, AWS Glue, AWS Lambda, and Amazon DynamoDB to process terabyte-scale data and support real-time access and orchestration across multiple systems. SQL - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Design and implement proactive data quality monitoring and alerting (e.g., anomaly detection) across production pipelines to safeguard downstream models Design and implement proactive data quality monitoring and alerting (e.g., anomaly detection) across production pipelines to safeguard downstream models
Spark Advance data engineering infrastructure using tools such as AWS Glue, Spark, Amazon EMR, and EMR Serverless to support science and analytics workloads must have Warehousing Experience with data modeling, warehousing and building ETL pipelines must have AWS Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions must have · not practised here Streaming You will work with AWS technologies such as Amazon EMR, Amazon S3, AWS Glue, AWS Lambda, and Amazon DynamoDB to process terabyte-scale data and support real-time access and orchestration across multiple systems. Machine learning Build and maintain big data pipelines for machine learning applications at Amazon scale, ensuring stability and performance across terabyte-sized datasets not practised here Airflow / orchestration You will work with AWS technologies such as Amazon EMR, Amazon S3, AWS Glue, AWS Lambda, and Amazon DynamoDB to process terabyte-scale data and support real-time access and orchestration across multiple systems. SQL - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Design and implement proactive data quality monitoring and alerting (e.g., anomaly detection) across production pipelines to safeguard downstream models Design and implement proactive data quality monitoring and alerting (e.g., anomaly detection) across production pipelines to safeguard downstream models