Job Description :
Implement and maintain a business glossary, metadata management, and/or data quality solution. Examples include SAP Information Steward, Collibra DG platform, Informatica Business Glossary / Metadata Manager / Quality Manager, etc. Preference on SAP products experience.
· When necessary, do data analysis and contribute to mapping of enterprise information architecture models to package application models, to determine impact or assess suitability of vendor solutions to enterprise information architecture requirements.
· When necessary, participate in the mapping and auditability of information assets as they flow from upstream legacy, package, or custom development and interfaces to downstream analytical applications, thus ensuring optimal transparency, quality, consistency and controlled redundancy across the enterprise.
· Contribute to the development of enterprise-wide Data Flow Diagrams & Business Information Models.
· Contribute to the creation of integrative and canonical views of data (i.e. semantic data services model) or information across multiple content types (structured, semi structured, XML or unstructured) to support convergence of information asset migration planning or impact analysis.
· Participate in the definition of technical standards and guidelines that pertain to data and information use, security, access, and governance.
Education & Special Trainings:
A BS in Computer Science, information systems or the equivalent is required.
Qualifications & Experience:
· 7+ years of experience in IT, with a minimum of two years in information systems design.
· In-depth experience designing and implementing information solutions.
· Data/information modeling expertise at the enterprise level.
· Experience with Modelling tools like ER/Studio, CA Erwin, Visio, Troux, or Sparx EA is required
· Understanding of common information architecture frameworks (such as Zachman and FEAF
· Understanding of differences between relational modeling and object modeling.
· Understanding of taxonomies and ontologies, as well as the challenges of applying structured techniques (data modeling) to less structured sources.
· Understanding of metamodels.