AWS Global Business Operations (GBO) team is looking for a data engineer to play a key role in building their industry leading Customer Information Analytics Platform. If you have experience in building and maintaining highly scalable data warehouses and data pipelines with high transaction volumes then we need you!!! The Data Engineer will design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures for business intelligence analytics and deep learning. Implement data ingestion routines both real time and batch using best practices in data modeling, ETL/ELT processes leveraging AWS technologies, Big data tools along with Oracle, and OLAP technologies. Provide on-line reporting and analysis using business intelligence tools and a logical abstraction layer against large, multi-dimensional datasets and multiple sources. Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions that work well within the overall data architecture. Produce comprehensive, usable dataset documentation and metadata. Provides input and recommendations on technical issues to the project manager.
7+ years of experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools. - Demonstrated strength in architecting data warehouse solutions and integrating technical components - 4+ years of experience with relational and star schema data modeling concepts - Strong analytical skills with excellent knowledge of Oracle, SQL and PL/SQL. - 4+ years of work experience with very large data warehousing environment - Excellent communication skills, both written and verbal
- Experience in gathering requirements and formulating business metrics for reporting. - Experience with AWS tool stack such as redshift and kinesis is preferred. - Experience in data science and tools such as R. - Experience with Informatica, OBIEE and Oracle is a plus - Expert understanding of ETL techniques and best practices to handle extremely large volume of data. - Good understanding of the cloud industry. - Python programming skills - Ability to leverage Big Data Technologies (EMR, Hadoop, Spark, streaming technologies)