Data Engineer
AWS Databases, Analytics and AI/ML Products and Services is one of the largest and fast-growing business units within AWS. We are working to rebuild and revolutionize data engineering and business intelligence systems within the database, analytics, and AI/ML organization to support fast-growing business needs. We are looking for experienced, self-driven Data Engineers. In this role, you will be building complex data engineering and business intelligence applications using the 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 a 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 big data stack, Python, Redshift, QuickSight, Glue/lake formation, EMR/Spark/Scala, Athena, etc.
• Develop and manage ETLs to source data from various commercial, sales, and operational systems and create a unified data model for analytics and reporting.
• Use business intelligence and visualization software (e.g., QuickSight) to develop dashboards that 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.
• Develop a deep understanding of vast data sources and know exactly how, when, and which data to use to solve particular business problems.
• Work with Product Managers, Finance, Service Engineering Teams, and Sales Teams on a day-to-day basis to support their new analytics requirements.
• 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.