Data Governance Consultant

Skills

Bachelor’s degree in Information Systems Finance Mathematics Operations Research Computer Science or equivalent/related degree required. Master’s preferred. At least 2+ years demonstrated experience helping organizations design or implement effective Data Management/Governance and/or Analytics programs in the cloud or on premises At least 2+ years’ experience leading project teams Success working in one of several Data Engineering/Management technology areas including but not limited to: Metadata management and data lineage utilizing technologies such as Collibra IBM’s Information Governance Catalog or Informatica’s Enterprise Data Catalog or others. Data Quality tools such as Informatica Data Quality (IDQ) SAS Data Management IMB InfoSphere Quality Stage or others. Master Data Management tools such as Informatica Oracle SAP MDG Profisee Collibra or others. Demonstrated experience working with Enterprise Data Management frameworks such as the EDM Council’s DCAM the Data Management Association’s DMBOK model or other overall Management frameworks. Overall knowledge and experience in dealing with Regulatory drivers for Enterprise Data Management such as CCPA / GDPR for Privacy along with ability to articulate how Management is applied in these cases to address the Compliance or Regulatory needs.

Description

Participate in the development, application, and adherence of data management policies and procedures across data governance, data quality, and master data management.
Responsible for applying the data governance roadmap including artifact discovery and development of data dictionaries, catalogues, classification policies, data lineage, operating models and standards.
Work directly with various stakeholders to realize the objectives identified in the data governance and data management roadmaps.
Articulate the value of and apply the DCAM methodology to drive business change through assessments and process improvement.
Formulate and deliver an innovative Data Strategy and Roadmap.
Think through data relationships, life cycles and processes to ensure and improve consistent data quality.
Support the importance of data processes, data integrity, cleanliness, and the strategic application of data.
Develop policy-based approaches to managing the evolution of an organization’s data throughout its life cycle from first contact and access to archival and destruction.
Build and formalize knowledge graphs and semantic modelling to articulate the interrelationships between business processes, systems, events, and entities.