AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. AWS Platform is the glue that holds the AWS ecosystem together. Whether its Identity features such as access management and sign on, cryptography, console, builder & developer tools, and even projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second.

Each day, thousands of developers make billions of transactions worldwide in the cloud. They are harnessing the power of Amazon Web Services (AWS) to enable innovative applications, websites, and businesses. However, there are always a few people that try to take unfair advantage of a good thing.

Are you interested in taking your skills and career to the next level, while having fun and fighting fraud in the cloud?
Would you like to be the driving force for developing the insights and strategy for our global AWS Fraud Prevention Team?


As a Data Engineer in the AWS Fraud Prevention Team, you will work alongside with top notch Business Analysts, Business Intelligence/ Data Engineers who work closely with the latest analytical databases (Redshift, RDS), reporting tools (Tableau/QuickSight) and other AWS services (Spectrum/Athena, Glue, S3). You’ll help the stakeholder teams comprised of data scientists, developers, fraud investigators and program managers to automate and optimize their processes. You’ll design and develop the end to end architecture for solving complex business problems and enabling data driven decisions via metrics.

Responsibilities Include:
· Collaborate and work with business owners, peer analysts/BIEs to understand data requirements to and build ETL to ingest the data into the centralized data warehouse.
· Design, create, and maintain data pipelines for large multi-dimensional datasets, across a variety of data platforms (RDS, Redshift, S3, Glue, Athena, Spectrum etc.).
· Develop and improve the current data analytics architecture, emphasizing data security, data quality and timeliness, scalability, and extensibility
· Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
· Deploy and use various big data technologies and run pilots to design low latency data architectures at scale