Amazon Sr Data Engineer Partner Analytics 

United States, Washington, Seattle 

203465403

As a Senior Data Engineer in the Global Partner Development org, you will provide technical leadership, lead data engineering initiatives, and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective. This role requires deep technical expertise combined with business acumen to build data pipelines from various data sources to enable Generative AI analytics solutions for our organization.Key job responsibilities
As a Senior Data Engineer, you will
• Design and implement data pipelines that will be used by Business Intelligence Engineers and Data Scientists for driving business insights and building ML models.
• Own the technical architecture of our BI and Data platforms.
• Use AWS technologies such as Redshift, Spectrum, S3, EC2, AWS Glue, etc. to optimize storage and compute resources for data infrastructure management.
• Work with the biggest Data Lakes in Amazon to publish the data for our partner teams and consume the data building analytical pipelines.
• Work closely with closely with Business Intelligence Engineers, Data Scientists, and Software Developers to gather data requirements and build data pipelines and data-sets to meet business needs.
• Own and support the management of our Redshift Cluster and QuickSight infrastructure.

- 5+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- 7+ years of data engineering, database engineering, business intelligence or business analytics experience
- Master's degree
- Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, etc.