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Join us for a role in " Data Modeller" at Barclays, where you'll spearhead the evolution of our digital landscape, driving innovation and excellence. You'll harness cutting-edge technology to revolutionize our digital offerings, ensuring unapparelled customer experiences. 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.
Job Responsibility:
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
Requirements:
Proven experience in data modelling, including conceptual, logical, and physical model design
Excellent technical skills (experience with JSON, SQL, Python) to maintain the data model and implement DataWalk solutions
Proficiency with graph-based platforms (e.g. DataWalk, Neo4j) and relational databases (e.g. SQL Server, Vertica)
Understand the application of graph theory in intelligence (e.g. link analysis, centrality)
Strong understanding of data architecture principles, data governance, and metadata management
Experience translating business requirements into technical data structures
Familiarity with data dictionaries, glossaries, and lineage documentation
Ability to work with structured and unstructured data sources
Nice to have:
Experience in threat intelligence, cybersecurity, or risk analytics domains
Experience designing or working with ontologies or semantic data models
Experience working in regulated environments or with sensitive data
Exposure to predictive modelling, ML/AI workflows, or scenario analysis