ASSPL
Business Intelligence, Data Engineering, finance
SrDataEngineerII,IESCFX&Prime
Neural analysis suggests this role is
optimal for Senior candidates.
“Sr Data Engineer II, IES CFX & Prime at ASSPL. Skills: Data engineering, Data warehousing, AWS technologies. Design platform for secured access. Implement platform for secured access”
What You'll Achieve.
Deliver BI and science applications/solutions; Ensure accurate and timely reporting
Industry & Context.
Analyze and solve problems at their root; Quantitative analysis; Business judgment
What They're Looking For.
Must Have
5+ years data engineering experience, 5+ years developing/operating large-scale data structures, 5+ years analyzing/interpreting data, Track record manipulating/processing/extracting value from large datasets
Nice to Have
Technical leadership experience, Mentoring experience for data engineering best practices
What You'll Do.
Design platform for secured access
Implement platform for secured access
Support platform for secured access
Interface with product/program managers
Deliver scalable DE solutions
Collaborate with BIEs and Data scientists
Enable production level data pipelines
Ensure data integrity
Ensure data model based design
Ensure data reliability
Perform thorough validation
Document data pipelines
Model data and metadata
Support ad-hoc reporting
Support pre-built reporting
Own design of metrics
Own development of metrics
Own maintenance of metrics
Own design of reports
Own development of reports
Own maintenance of reports
Own design of analyses
Own development of analyses
Own maintenance of analyses
Own design of dashboards
Own development of dashboards
Own maintenance of dashboards
Tune application performance
Tune query performance
Understand Amazon's data resources
Design data architecture
Scale data architecture
Improve reporting processes
Improve analysis processes
Automate support for datasets
Simplify support for datasets
Triage courses of action
Make quantitative analysis decisions
Make business judgment decisions
How You'll Work.
Team & Collaboration
Stakeholders across IES marketplaces; Product/program managers; BIEs and Data scientists
Full Job Description
Does the prospect of dealing with massive volumes of data excite you? Do you want to lead scalable data engineering solutions using AWS technologies? Do you want to create the next-generation tools for intuitive data access? Amazon's Customer Fulfillment Experience & Prime team needs a Data Engineer to shape the future of the Amazon data platform by working with stakeholders across IES marketplaces. The team is committed to building the next generation AI-first big data platform that will be one of the world's largest data warehouses to support Amazon's rapidly growing and dynamic businesses, and use it to deliver the BI and science applications/solutions which will have an immediate influence on day-to-day customer shopping and browsing behaviors. Members of the team will be challenged to innovate using the latest big data techniques. We are looking for a passionate data engineer to develop a robust, scalable data model and optimize the consumption of data sources required to ensure accurate and timely reporting for the Amazon businesses. You will share in the ownership of the technical vision and direction for advanced reporting and insight products. You will work with top-notch technical professionals developing complex systems at scale and with a focus on sustained operational excellence. We are looking for people who are motivated by thinking big, moving fast, and exploring business insights. If you love to implement solutions to hard problems while working hard, having fun, and making history, this may be the opportunity for you. Key job responsibilities Design, implement, and support a platform providing secured access to large datasets. Interface with product/program managers to gather requirements and delivering scalable DE solutions. Collaborate with BIEs and Data scientists in the team to enable production level data pipelines ensuring data integrity, data model based design, data reliability, thorough validation, and documentation. Model data and metadat
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