Metadata Architect

Embark on a transformative journey as a Meta Data Architect . At Barclays, our vision is clear –To define, direct and govern the bank’s target data architecture (inc. shared data environment, data model and standards) in support of business strategy, ensuring that data is accurate, secure and accessible to meet the needs of stakeholders.

To be successful as a Meta Data Architect, you should have experience with

Conceptual and Logical Data Modelling: Demonstrated experience in developing single, conceptual data models for large, multi-layered organisations, that accommodate sometime un-related data which would typically not be present in a generic model (e.g. climate data)

Data Modelling Tooling: Previous experience with digital data modelling tools such as Erwin, ER-Studios

Extensive Financial Experience: Have previous modelling experience within large, multinational financial services organisations, such as Insurance, Banking.

Some other highly valued skills may include

Previous experience using ERStudio to host data models and manage changes to it.

Ability to deliver multi-layered messages to audiences with varied levels of understanding in a clear and easy to understand manner.

Managing key risks, issues, stakeholders, and senior management Awareness of the governance processes required to capture key agreements and actions from senior, data related decision-making forums

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills

The location of your role is Knutsford/Glasgow/ Northampton

Purpose of the role

To define, direct and govern the bank’s target data architecture (inc. shared data environment, data model and standards) in support of business strategy, ensuring that data is accurate, secure and accessible to meet the needs of stakeholders.

Accountabilities

Development of the banks data architecture strategy, including the translation of bank-wide goals and objectives into target data architecture and transition plan.
Collaboration with stakeholders, including data operations, engineers and analysts, to provide subject matter expertise and share knowledge to promote standardised, consistent, safe and value-driven data usage.
Development and maintenance of the banks data architecture governance, standards and protection policies, regarding data models, authoritative data stores, and data capabilities, to support data quality, accuracy and consistency and the protection of sensitive information.
Management of the alignment of projects to the target data architecture through the provision of guidance, data solutions and monitoring of progress.
Definition of the shared reusable data capabilities, assets, tools and technologies required to connect disparate data sources, optimise data storage and provide seamless data access.
Custodianship of an overarching data model that directs how data is logically and physically structured within the banks physical data resources, e.g. database, interfaces and reports.
Monitoring applicable regulatory standards and industry developments for potential impact on the banks operations, controls and application portfolio.
Identification and selection of best-in-class data technologies and ongoing assessment of compliance with the bank's service level agreements and quality standards.