Responsibilities

Job Description – Data Modeller

Role Overview

We are looking for an experienced Data Modeller to design, develop, and maintain enterprise data models that support business, analytics, and technology requirements. The successful candidate will work closely with business stakeholders, data architects, data engineers, and application teams to translate complex business requirements into scalable and well-governed data models.

Key Responsibilities

• Design and maintain conceptual, logical, and physical data models aligned with business and technology requirements.
• Translate business requirements and data requirements into clear and scalable data structures.
• Develop data models for data warehouses, data lakes, operational systems, and analytics platforms.
• Define entities, attributes, relationships, hierarchies, keys, and data domains.
• Apply appropriate modelling techniques, including relational modelling, dimensional modelling (Star/Snowflake), and normalized/denormalized models.
• Work with Data Architects and Data Engineers to ensure models are optimized for scalability, performance, integration, and maintainability.
• Perform source-system analysis and support source-to-target mapping and data transformation requirements.
• Establish and maintain data dictionaries, metadata, naming standards, and modelling documentation.
• Support data governance initiatives by defining data standards, business definitions, ownership, lineage, and quality requirements.
• Review existing data models and identify opportunities for simplification, standardization, and optimization.
• Conduct data model reviews with technical and business stakeholders and incorporate feedback.
• Support database design and implementation activities across cloud and on-premise environments.
• Collaborate with engineering teams during development, testing, migration, and production implementation.
• Ensure data models comply with organizational architecture, security, privacy, and governance standards.

Required Skills & Experience

• Strong experience in Data Modelling, Data Architecture, or Data Management roles.
• Hands-on experience developing conceptual, logical, and physical data models.
• Strong understanding of relational database concepts, normalization, dimensional modelling, and database design.
• Experience with data modelling tools such as ERwin, ER/Studio, PowerDesigner, Sparx Enterprise Architect, or similar tools.
• Strong SQL knowledge and ability to analyze complex datasets and database structures.
• Experience with modern data platforms and technologies such as Snowflake, Databricks, Oracle, SQL Server, or PostgreSQL.
• Experience in working with one of the cloud platforms Azure, AWS, GCP(preferred)
• Understanding of data warehousing, ETL/ELT, data integration, and data lake/lakehouse architectures.
• Knowledge of metadata management, data lineage, master/reference data, and data governance principles.
• Ability to translate business terminology and processes into structured data definitions and models.
• Strong analytical, problem-solving, documentation, and stakeholder-management skills.

Desirable Skills

• Experience within banking, financial services, insurance, or other regulated industries (Preferred Private Banking)
• Familiarity with Data Vault, canonical data models, and industry-standard data models.
• Experience working with data cataloguing and governance platforms such as Collibra, Alation, Informatica, or Microsoft Purview.
• Knowledge of cloud-native data architectures and modern data engineering practices.
• Familiarity with Agile delivery methodologies and tools such as Jira and Confluence.
• Understanding of data privacy, regulatory, and information-security requirements.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related discipline, or equivalent professional experience.
• Relevant certifications in data management, cloud platforms, or data architecture are advantageous.

Key Competencies

• Data modelling and architecture
• Business and data requirements analysis
• Data governance and metadata management
• SQL and database design
• Data warehousing and analytics
• Stakeholder management
• Problem solving and attention to detail
• Technical documentation and communication