Description

Do you enjoy Big Data, working on cutting edge technologies or creating new/better, smarter algorithms layered on existing technology? Are you frequently looking for ways to help teams be more efficient?
The Data Center Finance team is looking for a talented Data Engineer to help build/ enhance the data landscape that shapes decision making and recommendations for our finance and business partners. You will help drive data architecture across many large datasets, perform exploratory data analysis and implement new data pipelines that feed into or from critical data systems at Amazon.
As a Data Engineer, you will develop new data engineering patterns that leverage new cloud architectures, and will extend or migrate existing data pipelines to the architectures as needed. You will also be assisting with integrating the Redshift platform as our primary processing platform to create a curated Data Center data model for the organization to leverage.
You will also be responsible for designing and implementing complex ETL pipelines on the data warehouse platform and other BI solutions to support a rapidly growing and dynamic demand for data, and use it to deliver data as a service, which will have an immediate influence on day-to-day decision making within AWS Infrastructure.


Key job responsibilities
• Design data schema and operate internal data warehouses and SQL/NOSQL database systems
• Design data models, implement, automate, optimize and monitor data pipelines
• Own the design, development and maintenance of ongoing metrics, reports, analyses, dashboards, etc. to drive key business decisions
• Analyze and solve problems at their root, stepping back to understand the broader context
• Manage Redshift/Spectrum/EMR infrastructure, and drive architectural plans and implementation for future data storage, reporting, and analytic solutions
• Work on different AWS technologies such as S3, Redshift, Lambda, Glue, etc. and Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
• Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
• Must possess strong verbal and written communication skills, be self-driven, and deliver high quality results in a fast-paced environment.
• Conduct rapid prototyping and proof of concepts
• Conceptualize and develop automation tools for bench marking data collection and analytics
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies