Enterprise Architect
Summary
Enterprise Data Architect at Manchester Airports Group (hybrid, Manchester Airport) developing MAG's logical and physical operational data model, a semantic data layer for safe AI consumption, and data governance practices, partnering with delivery teams and governance forums rather than focusing on analytics/reporting.
Summary
Enterprise Data Architect
Permanent
Hybrid ( Manchester Airport )
Why MAG?
- Bonus scheme
- 10% company contribution pension
- 25 days’ holiday plus bank holidays
- Free parking
- Subsidised public transport
- A wide range of company discounts
- Two volunteering days each year
- Free Virtual GP service, available 24 hours a day, seven days a week
- Care Concierge service
The role
Key accountabilities
- Build and extend MAG’s logical and physical enterprise data model, working from the existing conceptual framework of data domains, entities and relationships.
- Align the data model with relevant industry standards, including ACRIS, where appropriate.
- Develop a semantic data layer that supports safe, meaningful consumption of data by AI tools.
- Work with delivery teams to understand data sources and give data consumers a clear view of the context and meaning of the information they use.
- Provide data modelling and architecture expertise across airport and Group technology projects.
- Help develop MAG’s data consumption and management strategy, tooling and technical direction, then lead its implementation.
- Run MAG’s Enterprise Data Model Forum, providing an effective authority for proposed changes to the model.
- Represent data considerations within the Solution Design Authority and other technology governance forums.
- Strengthen data management practices covering lineage, ownership, stewardship, quality, security, retention, precedence rules and Master Data Management
What will make you successful in the role?
- Experience delivering enterprise logical and physical data models.
- Strong knowledge of data management, consumption and governance.
- A clear understanding of data lineage, ownership, stewardship, quality, security and retention.
- Experience adapting or extending industry-standard data models.
- Knowledge of technologies and third-party tools used to manage and provide access to data.
- An understanding of semantic data layers and how they can support accurate, responsible use of data by AI tools.
- Knowledge of data integration techniques, data warehousing and business intelligence technologies.
- Strong analytical and problem-solving skills, with the ability to turn business needs into effective data solutions.
- The communication and stakeholder-management skills to explain complex data concepts clearly to both technical and non-technical audiences.
- Experience contributing to an Enterprise Data Model Forum or a similar data-governance structure.
Equal Opportunties and Reasonable Adjustments
MAG