Identify and provide solutions to complex business critical issues identified within large customer analytic landscapes. Analyze performance and product solution issues to provide recommendations for optimization with a focus on data extraction, modeling, and visualization. In-depth understanding of enterprise data warehouse architecture/optimization/methodology spanning data extraction frameworks, structural/data analysis, data transformation/flow, and query definition. Provide insights to customers on best practices and proof of concepts to expose new solution capability and efficiency. Architect and provide best practices around data warehousing methodology for both relation & non-relational databases. Architect object-oriented applications to expedite analysis/delivery through proof of concepts and internal custom applications.Engage, optimize, and architect analytic landscapes for customers within Global Sponsorships. Currently architecting, designing models, and managing a greenfield data warehouse implementation. Driving innovation and data driven business decisions through unified reporting - technically and functionally. Topics span from connecting/configuring source systems, establishing sustainable data models, and exploring data through front end visualizations. Extend databases through Python/Java to perform advanced algorithms. Advanced ODBC/JDBC configuration, data warehousing architecture and modeling, and OS level architecture.Supported Customers : San Jose Sharks, SAP Arena – Sharks Sports & Entertainment, Merck, Ford, McKesson, ATB Financial, Costco, CSL Behring, FBL Financial Group, Medtronic, International Flavors & Fragrances, Thompson Reuters, Home Depot, BD, Intel, Marathon Petroleum, Southern California Edison, Stryker, Bristol Myers Squibb