Data Modeller

Join us as a Data Modeller at Barclays, where you will design, implement, and maintain conceptual, Logical and Physical data models that meet business data/process and technology requirements, by using designs and data strategies across a wide selection of platforms.

To be successful as Data Modeller you should have:

• Experienced in Conceptual, Logical and Physical Data Modelling - different types of tables, meaning, concepts, should be able to explain real-world scenarios with use-cases.
• Excellent SQL Skills, Ability to do data cleansing/quality checks and data profiling.
• Good understanding of Data Flow from Source to Target, Data Lineage, ETL concepts and Data Transformation.

Some other highly valued skills may include:

• Good understand Data Warehousing, Data Marts, Data Lake concepts, Medallion Architecture, exposure to Cloud Data Platforms (Preferably AWS).
• Good understanding of Requirements, Stakeholder Management, ability to understand Business Functionality.
• Data Governance, Metadata Management, Reporting Layer concepts.

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.

This role will be based in Glasgow.

To design, implement, and maintain conceptual, Logical and Physical data models that meet business data/process and technology requirements, by using designs and data strategies across a wide selection of platforms.

• Analysis and documentation of business requirements to translate them into data models aligned with organisational goals.
• Development and maintenance of data dictionaries and glossaries to define data elements and their usage.
• Analysis and monitoring of data usage patterns to identify opportunities for data optimisation and improvement, in partnership with the Data Base Administrator.
• Strategic architecture definition and product selection.
• Production of logical designs in relevant subject area (technical, data, operational), showing for example: processes, objects, data flows, inputs, stored data and outputs. Identifying common components.
• Implementation of architectures and Identification, ownership and resolution of design related issues.
• Definition and documentation of data architectures standards, principles and strategies.