About this role
Role: Data Modeler Key Responsibilities Enterprise Data Modelling & Architecture (Primary Focus)
• Support the Lead Enterprise Data Architect in the creation, refinement, and maintenance of enterprise‑level data models. • Support the data models governance process, ensuring standards, naming conventions, and modelling quality. • Support and maintain the ER/Idera modelling repository, including version control and model lineage. • Support projects from a data architecture perspective, ensuring alignment with enterprise modelling practices. • Build the necessary material and define repeatable processes to run the Data Architecture Assurance Service. • Contribute to data dictionaries, business glossaries, and enterprise metadata assets. • Contribute to the creation of data flows, end‑to‑end lineage, and cross‑domain data relationships. • Support data definitions working groups and other data governance bodies, ensuring consistency and traceability. Data Platform & Engineering Support
• Ensuring alignment with data architecture standards. • Support definition of data schemas, ingestion patterns, and ETL/ELT solutions for consumption, validation, storage, and quality control. • Shape and embed data management best practices across engineering teams (lineage, quality, storage design, master/reference data). • Support the design and implementation of metadata capabilities, including lineage, business glossary, technical metadata, and stewardship workflows. • Contribute to data harvesting efforts and support the implementation of business catalogue tools. Key Technical Skills & Experience
• Strong data modelling skills across conceptual, logical, and physical layers. • Hands‑on experience with the Microsoft data technology stack (e.g., Azure SQL, Synapse, ADF, Databricks). • Experience in data collection, integration, and management across distributed systems. • Understanding of defining data schemas and ETL/ELT solutions for consumption, validation, and secure storage of data. • Proven experience in designing and implementing metadata management capabilities (lineage, glossary, metadata store, cataloguing tools). • Experience with data definitions creation and stewardship activities. • Strong business mindset to interpret organizational business capabilities and translate them into data domains and entities. • Experience in data harvesting, business catalogue, and data discovery tools. • Deep understanding of data management concepts including: • Traceability • Lineage • System of Record (SoR) and System of Authority (SoA) • Master Data and Reference Data • Metadata lifecycle and curation
Role: Data Modeler Key Responsibilities Enterprise Data Modelling & Architecture (Primary Focus)
• Support the Lead Enterprise Data Architect in the creation, refinement, and maintenance of enterprise‑level data models. • Support the data models governance process, ensuring standards, naming conventions, and modelling quality. • Support and maintain the ER/Idera modelling repository, including version control and model lineage. • Support projects from a data architecture perspective, ensuring alignment with enterprise modelling practices. • Build the necessary material and define repeatable processes to run the Data Architecture Assurance Service. • Contribute to data dictionaries, business glossaries, and enterprise metadata assets. • Contribute to the creation of data flows, end‑to‑end lineage, and cross‑domain data relationships. • Support data definitions working groups and other data governance bodies, ensuring consistency and traceability. Data Platform & Engineering Support
• Ensuring alignment with data architecture standards. • Support definition of data schemas, ingestion patterns, and ETL/ELT solutions for consumption, validation, storage, and quality control. • Shape and embed data management best practices across engineering teams (lineage, quality, storage design, master/reference data). • Support the design and implementation of metadata capabilities, including lineage, business glossary, technical metadata, and stewardship workflows. • Contribute to data harvesting efforts and support the implementation of business catalogue tools. Key Technical Skills & Experience
• Strong data modelling skills across conceptual, logical, and physical layers. • Hands‑on experience with the Microsoft data technology stack (e.g., Azure SQL, Synapse, ADF, Databricks). • Experience in data collection, integration, and management across distributed systems. • Understanding of defining data schemas and ETL/ELT solutions for consumption, validation, and secure storage of data. • Proven experience in designing and implementing metadata management capabilities (lineage, glossary, metadata store, cataloguing tools). • Experience with data definitions creation and stewardship activities. • Strong business mindset to interpret organizational business capabilities and translate them into data domains and entities. • Experience in data harvesting, business catalogue, and data discovery tools. • Deep understanding of data management concepts including: • Traceability • Lineage • System of Record (SoR) and System of Authority (SoA) • Master Data and Reference Data • Metadata lifecycle and curation
Role: Data Modeler Key Responsibilities Enterprise Data Modelling & Architecture (Primary Focus)
• Support the Lead Enterprise Data Architect in the creation, refinement, and maintenance of enterprise‑level data models. • Support the data models governance process, ensuring standards, naming conventions, and modelling quality. • Support and maintain the ER/Idera modelling repository, including version control and model lineage. • Support projects from a data architecture perspective, ensuring alignment with enterprise modelling practices. • Build the necessary material and define repeatable processes to run the Data Architecture Assurance Service. • Contribute to data dictionaries, business glossaries, and enterprise metadata assets. • Contribute to the creation of data flows, end‑to‑end lineage, and cross‑domain data relationships. • Support data definitions working groups and other data governance bodies, ensuring consistency and traceability. Data Platform & Engineering Support
• Ensuring alignment with data architecture standards. • Support definition of data schemas, ingestion patterns, and ETL/ELT solutions for consumption, validation, storage, and quality control. • Shape and embed data management best practices across engineering teams (lineage, quality, storage design, master/reference data). • Support the design and implementation of metadata capabilities, including lineage, business glossary, technical metadata, and stewardship workflows. • Contribute to data harvesting efforts and support the implementation of business catalogue tools. Key Technical Skills & Experience
• Strong data modelling skills across conceptual, logical, and physical layers. • Hands‑on experience with the Microsoft data technology stack (e.g., Azure SQL, Synapse, ADF, Databricks). • Experience in data collection, integration, and management across distributed systems. • Understanding of defining data schemas and ETL/ELT solutions for consumption, validation, and secure storage of data. • Proven experience in designing and implementing metadata management capabilities (lineage, glossary, metadata store, cataloguing tools). • Experience with data definitions creation and stewardship activities. • Strong business mindset to interpret organizational business capabilities and translate them into data domains and entities. • Experience in data harvesting, business catalogue, and data discovery tools. • Deep understanding of data management concepts including:
• Traceability • Lineage • System of Record (SoR) and System of Authority (SoA) • Master Data and Reference Data • Metadata lifecycle and curation #LI-KS1 #KGS