Data Modeler

About the Role

We are looking for a detail-oriented Data Modeler – Level 1 to support the design, development, documentation, and maintenance of conceptual, logical, and physical data models for enterprise data platforms. The ideal candidate should have a good understanding of relational and dimensional modelling, data warehousing concepts, cloud data platforms, and data modelling best practices. The role involves collaborating with business analysts, data engineers, architects, and BI developers to translate business requirements into scalable, reliable, and performance-efficient data models that support reporting, analytics, and business intelligence.

Key Responsibilities

• Design and maintain conceptual, logical, and physical data models based on business and technical requirements.
• Develop relational and dimensional models, including fact tables, dimension tables, star schemas, and snowflake schemas.
• Define data entities, attributes, relationships, primary keys, foreign keys, surrogate keys, and business keys.
• Apply normalisation principles (1NF, 2NF, and 3NF) and appropriate denormalisation techniques.
• Define fact table grain, measures, dimensions, and relationships to ensure accurate reporting and prevent double counting.
• Implement Slowly Changing Dimensions (SCD Types 1, 2, and 3) to support historical data tracking.
• Support data warehouse and data mart development using established modelling methodologies such as Kimball and, where applicable, Data Vault.
• Work with data engineers to design data ingestion, incremental loading, Change Data Capture (CDC), and late-arriving data handling strategies.
• Contribute to layered data architecture, including raw, curated, and business-ready layers (Bronze, Silver, and Gold).
• Support data modelling on cloud platforms and warehouses such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, or equivalent technologies.
• Assist in physical database design decisions, including partitioning, clustering, distribution, and query performance optimisation.
• Model semi-structured data, including JSON and nested or repeated data structures.
• Follow data governance, security, and privacy standards, including PII classification, column masking, and row-level access controls.
• Maintain data dictionaries, business glossaries, naming conventions, model documentation, and metadata.
• Use data modelling tools to create, compare, validate, and maintain models and generate DDL where applicable.
• Manage model changes through version control, peer reviews, schema comparison, and impact analysis.
• Collaborate with business stakeholders to validate definitions, relationships, reporting requirements, and data quality expectations.

Requirements & Tech Stack

• Strong understanding of relational database concepts and SQL.
• Knowledge of conceptual, logical, and physical data modelling.
• Understanding of normalisation, denormalisation, keys, relationships, and cardinality.
• Working knowledge of dimensional modelling, including facts, dimensions, grain, star schemas, and snowflake schemas.
• Understanding of Slowly Changing Dimensions (SCD Types 1, 2, and 3).
• Knowledge of data warehouse architecture, data marts, ETL/ELT pipelines, and OLTP versus OLAP systems.
• Familiarity with incremental data loading, CDC, upserts, and data reconciliation.
• Basic understanding of cloud data platforms and modern lakehouse architectures.
• Knowledge of database performance concepts, including partitioning, clustering, and query optimisation.
• Familiarity with data security, data governance, metadata management, and data lineage.
• Ability to create and maintain data models using tools such as ERwin, ER/Studio, SAP PowerDesigner, SQLDBM, Lucidchart, or draw.io.
• Familiarity with Git or similar version control tools and database schema change management.

Preferred Skills

• Hands-on experience with at least one cloud data warehouse, such as Snowflake, BigQuery, Redshift, or Azure Synapse Analytics.
• Exposure to dbt, data cataloguing tools, and automated data lineage.
• Basic understanding of Data Vault modelling.
• Experience working in Agile development environments.
• Exposure to BI tools such as Power BI or Tableau.
• Understanding of data quality rules, audit columns, and historical data retention.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• Relevant experience in data modelling, database development, data engineering, or data warehousing.
• Relevant certifications in cloud data platforms or database technologies are an advantage but not mandatory.

Key Competencies

• Analytical thinking and problem-solving.
• Strong attention to detail and data accuracy.
• Clear documentation and communication skills.
• Ability to understand business requirements and translate them into data structures.
• Collaboration with cross-functional technical and business teams.
• Willingness to learn new data modelling methods and cloud technologies.

Expected Deliverables

• Conceptual, logical, and physical data models.
• Fact and dimension models for reporting and analytics.
• Data dictionaries, metadata documentation, and naming standards.
• Model-to-database validation and schema comparison reports.
• Data lineage and impact analysis documentation.
• Recommendations for model improvements, data quality, and query performance.

Success Criteria

• Data models accurately represent agreed business requirements.
• Fact table grain and relationships are clearly defined and validated.
• Models support reliable reporting and minimise duplication and double counting.
• Model documentation follows organisational standards.
• Changes are reviewed, traceable, and aligned with downstream data consumers.
• Data models support maintainability, scalability, security, and performance.