Data Engineer II, Amazon Key

We are actively seeking a motivated and multi-talented individual who is passionate, highly autonomous and have deep expertise in the design, creation, and management of large and complex datasets. You should be an authority at crafting, implementing, and operating stable, scalable solutions to flow huge amounts of data from production systems into the Redshift cluster, and build complex transforms to compute and store new data developed by the software teams.

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 unified data model for analytics and reporting.
• 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.
• Develop 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, Engineering, BI Teams on 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.

About the team

Amazon Key team's mission is to provide Amazon with 1-click access to every customer"s doorstep. We are inventing the next-generation smart delivery operation technologies in IoT (Internet-of-Things). We develop technology-based solutions matching customer needs and delivery capacity with precision and efficiency, and expanding and transforming delivery experience with unprecedented quality, productivity and scale.