Amazon Data Engineer II Global Procurement Program GPP 

United States, Texas, Austin 

401524348

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.
Austin, TX, USA

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience with SQL