• Data Marketplaces• Data Products• Data Monetization• Data Contracts• Data Mesh• Data Vault• AI-Assisted Data Architecture and Management

*Agile and flexible Information modeling techniques that organizes data by business concepts*Applied Information model to span the entire enterprise data landscape including internal data stores and external data sources. Created a map to the enterprise data fabric*Used the Information model to produce analytic and API data sets*Applied the Information model as a logical layer to the central data hub and data virtualization framework*Cloud Data Architecture and Analytics*Data Vault 2.0*Cloud Data Warehouse*DataOps*Data Management, Governance, and Enablement*AWS Cloud Platform

*Data Warehouse and Analytics*Master Data Management

Ingesting data from all Sports Authority sources into a single Google Cloud Platform instance; thus, providing a high volume of data from the entire, large organization in one place and one platform. Data blending and analysis possibilities span all aspects of the business. Using Tableau for metadata cataloging and data analysis.-Working closely with business associates to develop self service analytics enabled with non-invasive data management approaches-Google Cloud Platform, Hadoop and central data staging (data lake)-New master data management (MDM) technology stacks with graph databases and API services (Reltio)-Self service data blending, data profiling and statistical analysis (Alteryx and R)-Cloud based data services with the goal of greatly reducing IT costs-"Big Data" architecture and road map to include data warehousing-Cloud based Integration Platform as a Service (iPaaS): API management and data virtualization-Areas of analysis include customer experience, customer segmentation, supply chain, logistics, eCommerce-Data visualization with Tableau-Take advantage of the cloud platform low cost and rapid scalability to create "Data Science" sandboxes for more involved statistical models and analysis- Structure data self service with governance rules to ensure integrity of outcomes and results (analyze data quality and check validity of statistical analysis)

Primary effort for 2015: Implement IBM PureData System for AnalyticsDeveloping Enterprise Data Architecture Strategy To Support • Business goals and objectives • Management of information as a corporate asset • Access to information • Information UsageUsing • Master Data Management • Metadata Management • Agile and Flexible Analytic Data Stores (Big Data, Data Science) • Data modeling for architectural reuse, agility and flexibility • Hadoop and NoSQL • Data and Information Integration using Data Virtualization and Data Services • Statistical Analysis and Predictive Analytics (R, SPSS, SAS) • Structured and Unstructured Information Architecture and Strategy • Incorporation of architecture strategy into agile methodology

Responsible for architecture and strategy of the identity management system. Key to the strategy is integrating numerous acquired online data and information products. Standardizing product and customer master data and providing integration in order to give customers a seamless online experience while providing quality data for internal analytics and business intelligence. * Identity management data quality and governance * Object to relational data modeling * Logical and physical data modeling * Data services through Denodo Data Virtualization * Product and Customer master data management * Service Oriented Architecture * Integrating architecture strategy with agile development methodology

Principal data architect for the Enterprise Information Management effort. Working on a three-year enterprise architecture roadmap and establishment of an enterprise architecture program. Architecture components, strategies and initiatives include:• Enterprise architecture and information management strategy• Establish data modeling standards and practices• Master data management strategy• Metadata management strategy• Data integration strategy: use data virtualization to reduce development and support costs while increasing flexibility• Strategically position structured and unstructured information• Work with management and executive levels of the organization to position information and contribute to the success of corporate goals and objectives• Data warehouse• SalesForce.com integration through data virtualization• Service oriented information access• Partnered with business teams to develop data governance and quality programs

• Consulted for Wells Fargo on a customer contact data and business intelligence architecture. Created an enterprise PowerDesigner environment with architecture design processes. The environment included the following: requirements, conceptual, logical, physical, information liquidity and multi-dimensional models, data lineage, impact analysis and metadata. The environment also has a central repository with design collaboration and version control. • Provided consultation in regard to consolidating the business intelligence environments. Solutions included BusinessObjects, Hyperion, MS Reporting services. The primary objective is to architect a single, cost effective enterprise wide solution.• Additional projects included:Designing and building configurable web apps based on disciplined investment processes to consolidate information, ease tracking and analysis and streamline decision making. Using open source BI tools and API’s for securities, futures and commodities and information provided by investment data API's.

Managed a team to successfully establish a new enterprise data and information management program. Also served as the principal enterprise data architect.• Developed an enterprise architecture road map and blueprint, integrating all business and technology architecture activities into a customized TOGAF framework.• Worked with PMO to incorporate architecture standards into the project management process.• Led the Data Services team in successfully building an enterprise data warehouse foundation and jump-started the organization’s BI program. • Started a Business Intelligence Competency Center, which addressed enterprise wide requirements, education, resource allocation, initiative prioritization and consolidation of business, technical and analytical expertise.• Business Intelligence architecture included: operational, analytical, predictive, business intelligence competency center (BICC).• Designed a hub based master data strategy with a master data service along with metadata management. An ESB for data integration was used with a common business entity canonical model.• Team performance reviews, project management, resource management, individual coaching and development, team development, vendor management, vendor selection and management of a $1.6 million budget.

Main accomplishments included life cycle management of the existing data warehouse, improving information access, quality and reliability through master data and metadata management and a data quality program.• Served as primary architect for developing the data access service for the Shared Travel Platform. Modeling activities involved the logical and physical design for a large service oriented travel services transaction platform.• Managed technology lifecycle through enterprise architecture methods.• Aligned business and IT through enterprise architecture.• Data architecture and design, data warehouse modeling and design, business intelligence and data access, database development and deployment, design of the data access layer for the service oriented architecture (SOA), metadata management, master data management, architecture strategy and direction.• Resource planning, project management, performance reviews, budgeting and finance, vendor management, reported to the VP level.

Primarily consulted at Galileo in regard to the enterprise data model, data warehouse, data access for the Shared Travel Platform and overall information strategy.In addition to consulting work at Galileo I developed information strategies for private investors.

Successful in managing data architecture across the enterprise, providing technology solutions to meet business objectives and exploit opportunities.• Data warehouse – Architected ETL from multiple data sources, main data warehouse, ODS, data marts, star schema designs and the metadata repository.• Business intelligence – Architected OLAP, Multi-dimensional analysis, statistical data mining and BI delivery and access.• EAI – Architected Tibco to transform trading systems from batch processes to a real-time event driven messaging environment.• Enterprise architecture and technology lifecycle management using the Zachman framework.• Vendor research, analysis, selection, negotiation and management.• Requirements gathering, executive briefings and technology workshops.

Business analysis, data warehousing and business intelligence. Provided business analysis support for the management of 53 cable system for engineering, marketing and operations. Provided business intelligence to all levels of management including executive.

Database design, data feed and data integration. Managed IT infrastructure for futures and commodities trading advisors.

Designed and built systems for data collection, operations and analysis used in manufacturing processes. Developed systems and programs for manufacturing statistical process control.

Designed and developed systems to collect data from the chemical manufacturing process for analysis. Developed systems and programs for manufacturing statistical process control.