Senior Data Engineer, Amazon

• We are looking for an exceptional data professional with deep expertise in distributed data systems and big data technologies on AWS
• 5+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
• Knowledge of distributed systems as it pertains to data storage and computing
• Experience building production data pipelines and processing frameworks on AWS

Desirable

• The Data Operations and Engineering team in the Application Security Organization is seeking a creative, curious, and ambitious Senior Data Engineer to design, build and optimize the data infrastructure that powers Amazon's security monitoring and analytics solutions
• You will play a key role in building highly scalable and reliable data pipelines that ingest, process and serve security data at a massive scale across Amazon's systems
• In this critical role, you will collaborate closely with Industry Specialists, Software Engineers, Security Engineers, and Product Managers to understand data requirements
• You will architect data models, build ETL/ELT pipelines, and implement data distribution layers that enable advanced security use cases like threat detection, incident response, forensic analysis and data exploration
• With your keen data engineering skills, you will empower AWS SOC to derive insights from our security telemetry data and drive continuous improvement of Amazon's security posture
• This is a unique opportunity to join a world-class team and have a direct impact on safeguarding Amazon's infrastructure and customer trust
• You will thrive in our dynamic environment by driving innovation, setting best practices, and mentoring other engineers
• Architect and build scalable data pipelines for ingesting, processing and serving security data from diverse sources across Amazon
• Design and implement data models, storage solutions and distributed processing frameworks for security analytics workloads
• Collaborate with data scientists to build features, transformations and serving layers for machine learning models
• Optimize data engineering systems for performance, cost, reliability, recoverability and security
• Automate data quality monitoring, testing and validation processes
• Partner with software engineering teams to seamlessly integrate data solutions with security applications and services