Data Architect
ISS - Information Systems Services
Salary: £45,585 - £56,021
Closing Date: Monday 5th August 2024
Interview Date: Week commencing: Monday 12th August 2024
The Opportunity
To work closely with stakeholders across the University and within ISS to understand data requirements and needs, leading on the end-to-end design of the University's data platforms, integrations, and architecture.
The role-holder will also develop best practice data management and governance processes, to support the security, reliability, availability, and efficient use of the University's data. They will design conceptual/logical data models, flows, integrations, APIs, schemas, standards, and policies. Working with data integration and development teams, they will also architect robust data pipelines, workflows, and integrations between systems.
What you will need
Experience in database development, data management, data integration, and data storage and retrieval techniques to optimise performance.
Proven experience of developing data architectures in a complex organisation
Experience of identifying technical and business challenges and designing scalable and flexible data architectures to provide the information needed to address those challenges.
Please refer to the included job description and person specification for further details of essential and desirable qualifications for the role.
Major Duties:
Design scalable, flexible, and high-performance data architectures that meet business objectives. Develop and maintain conceptual, logical, and physical data models for various data platforms and technologies.
Lead the creation of a common data platform, ensuring seamless integration and data flow across different systems and applications.
Design and develop robust data models, schemas and data repositories that optimise data storage, retrieval, and performance.
Oversee the architecture of integrations and data APIs, ensuring efficient and secure data exchange between different systems, including both on premise and cloud/SaaS platforms, utilising ETL processes.
Implement data quality and validation processes to ensure accuracy, completeness, and consistency of data. Analyse the effectiveness and efficiency of existing and proposed data components, identifying opportunities for optimisation.
Ensure that any design, build, decommissioning and archiving of any data is compliant with data compliance and data policies.
Ensure data security, privacy, and regulatory compliance across all data solutions. Implement best practices for data governance and data management