ExecutivePlacements.com
Overview
Sales, Marketing and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers, including public sector. The AWS Marketing Data: AI Science, Analytics and Engineering team owns analytics, reporting and self-service tooling, data representation, machine learning models, measurement, valuation and economics products for AWS Marketing. As a Data Engineer at AWS, you will work in a large, extremely complex and dynamic data warehousing environment, integrating multiple heterogeneous data sources with the AWS Marketing Data Warehouse - Jarvis and building efficient, flexible, and scalable data warehouse and reporting solutions. You will partner with business owners, develop key business questions, and build the data sets that answer those questions, while operating stable, scalable, low-cost solutions to move data from production systems into the data warehouse and reporting applications. Location: Seattle, WA Key Responsibilities
Design, implement, and support a platform providing ad-hoc access to large datasets Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL Build robust and scalable data integration (ETL) pipelines using SQL, Python and AWS services such as Data Pipelines, Glue Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL/Redshift Interface with business customers, gathering requirements and delivering complete reporting solutions Build and deliver high quality datasets to support business analyst and customer reporting needs Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers Basic Qualifications
3+ years of data engineering experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience Experience with data modeling, warehousing and building ETL pipelines Preferred Qualifications
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) Seniority Level
Mid-Senior level Employment Type
Full-time Job Function
Information Technology Industry
Advertising Services Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers.
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Sales, Marketing and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers, including public sector. The AWS Marketing Data: AI Science, Analytics and Engineering team owns analytics, reporting and self-service tooling, data representation, machine learning models, measurement, valuation and economics products for AWS Marketing. As a Data Engineer at AWS, you will work in a large, extremely complex and dynamic data warehousing environment, integrating multiple heterogeneous data sources with the AWS Marketing Data Warehouse - Jarvis and building efficient, flexible, and scalable data warehouse and reporting solutions. You will partner with business owners, develop key business questions, and build the data sets that answer those questions, while operating stable, scalable, low-cost solutions to move data from production systems into the data warehouse and reporting applications. Location: Seattle, WA Key Responsibilities
Design, implement, and support a platform providing ad-hoc access to large datasets Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL Build robust and scalable data integration (ETL) pipelines using SQL, Python and AWS services such as Data Pipelines, Glue Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL/Redshift Interface with business customers, gathering requirements and delivering complete reporting solutions Build and deliver high quality datasets to support business analyst and customer reporting needs Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers Basic Qualifications
3+ years of data engineering experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience Experience with data modeling, warehousing and building ETL pipelines Preferred Qualifications
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) Seniority Level
Mid-Senior level Employment Type
Full-time Job Function
Information Technology Industry
Advertising Services Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers.
#J-18808-Ljbffr