Senior Data Modeller

Were looking for an experienced Data Modeller who enjoys uncovering how data really works within complex enterprise systems.

A key part of this role involves analysing existing source systems understanding their data models and mapping how information flows across platforms. Using SQL data profiling techniques and occasionally Python youll investigate datasets discover relationships document business entities and build a detailed understanding of the underlying data landscape.

Youll use these insights to design target data models that bring together data from multiple source systems into a coherent structure that supports software solutions reporting analytics and AI-driven capabilities.

A key aspect of the role is ensuring that target models are not only functionally correct but also performant. Youll need to understand how modelling decisions impact query performance scalability data ingestion patterns and application behaviour across graph-database and relational database platforms.

This is a modelling and analysis-focused role rather than a traditional data engineering role. Success comes from understanding complex business domains discovering how data is organised and translating that understanding into robust scalable solution-focused data designs.

• Analyse source systems to understand existing data structures relationships data quality and business meaning.

• Use SQL and data profiling techniques to investigate datasets validate assumptions and uncover hidden dependencies.

• Reverse engineer and document existing data models where formal documentation is incomplete or unavailable.

• Collaborate with business stakeholders and subject matter experts to understand how data is created maintained and consumed.

• Identify common business entities and relationships across multiple source systems.

• Design conceptual logical and physical target data models that consolidate and harmonise data from disparate source systems.

• Design graph database models that accurately represent complex relationships and support efficient traversal and query patterns.

• Define source-to-target mappings and transformation requirements.

• Evaluate modelling decisions against performance scalability and operational requirements.

• Create and maintain data dictionaries business glossaries entity definitions and modelling documentation.

• Assess the impact of source-system changes on downstream systems and target models.

• Work closely with architects and engineering teams to ensure models are implemented consistently and accurately.

• Champion best practices in data modelling governance metadata management and data quality.

• Experience creating and implementing target graph database models using technologies such as Neo4j Spanner Graph Gremlin or similar graph platforms.

• Proven experience analysing and modelling complex enterprise data landscapes.

• Strong SQL skills with the ability to query profile explore and validate large datasets.

• Experience investigating existing source systems and developing an understanding of undocumented or poorly documented data structures.

• Strong conceptual logical and physical data modelling skills.

• Proven experience designing target data models that integrate data from multiple source systems.

• Proven experience designing optimised relational data models.

• Strong understanding of graph modelling concepts including nodes relationships traversal patterns and performance considerations.

• Understanding of how data model design affects system performance scalability query efficiency and application behaviour.

• Experience balancing business requirements with performance and operational constraints when designing target models.

• Experience working with cloud platforms such as Azure AWS or GCP.

• Ability to identify and model business entities relationships hierarchies and business rules.

• Experience producing source-to-target mappings and transformation specifications.

• Strong analytical and problem-solving skills.

• Excellent stakeholder engagement and communication skills.

• Experience with Google Cloud Platform (GCP) particularly BigQuery Cloud Spanner and Spanner Graph.

• Telecoms domain experience particularly OSS/BSS network inventory or service assurance platforms.

• Experience with Master Data Management (MDM) and reference data management.

• Familiarity with metadata management lineage and data cataloguing tools.

• Experience supporting data models for AI knowledge graph or machine learning solutions.

• Python for data exploration profiling reconciliation and data quality analysis.