The AWS Network Finance and Data Transfer Data Engineering team provides foundational and centralized data platform for AWS Finance (supporting AWS Networking, AWS CloudFront, AWS Direct Connect) to identify financial insights for better understanding of our customers and costs. Our teams take on some of the hardest scalability, performance, and distributed computing challenges. We process big data and provide tools for customers to interactively understand the copious amounts of data we store.

We are looking for experienced, self-driven Data Engineer. In this role, you will be building complex data engineering and business intelligence applications using AWS big data stack. You should have deep expertise and passion in working with large data sets, data visualization, building complex data processes, performance tuning, bringing data from disparate data stores and programmatically identifying patterns. You should have excellent business acumen and communication skills to be able to work with business owners to develop and define key business questions and requirements. You will provide guidance and support for other engineers with industry best practices and direction. Amazon Web Services (AWS) has culture of data-driven decision-making, and demands timely, accurate, and actionable business insights.

Key job responsibilities
• Design, implement, and support data warehouse/ data lake infrastructure using AWS bigdata stack, Python, Redshift, QuickSight, Glue/lake formation, EMR/Spark, Athena etc.
• Develop and manage ETLs to source data from various financial, AWS networking and operational systems and create unified data model for analytics and reporting.
• Creation and support of real-time data pipelines built on AWS technologies including EMR, Glue, Redshift/Spectrum and Athena.
• Collaborate with other Engineering teams, Product/Finance Managers/Analysts to implement advanced analytics algorithms that exploit our rich datasets for financial model development, statistical analysis, prediction, etc.
• Continual research of the latest big data and visualization technologies to provide new capabilities and increase efficiency.
• Use business intelligence and visualization software (e.g., QuickSight) to develop dashboards those are used by senior leadership.
• Empower technical and non-technical, internal customers to drive their own analytics and reporting (self-serve reporting) and support ad-hoc reporting when needed.
• Working closely with team members to drive real-time model implementations for monitoring and alerting of risk systems.
• Manage numerous requests concurrently and strategically, prioritizing when necessary
• Partner/collaborate across teams/roles to deliver results.
• Mentor other engineers, influence positively team culture, and help grow the team.