Data Modeller

We are looking for a Senior Data Modeller to support a Microsoft Fabric data platform program for a large enterprise client. This role will help shape the data models that sit at the heart of that approach. The successful candidate will work across source system onboarding, Fabric Lakehouse and Warehouse design, reusable Silver-layer datasets, Gold-layer analytical models and Power BI semantic consumption.

The role is not limited to drawing data models. It requires someone who can understand business processes, challenge unclear definitions, define model grain, agree with common entities and help engineering teams turn source data into trusted, usable data assets. This would suit someone with strong dimensional modelling experience who has worked on modern cloud data platforms and is comfortable operating between business stakeholders, architects, engineers, governance teams and BI/reporting users.

What You Will Be Doing

• The Data Modeller will be involved in the design of enterprise and domain-level data models across Microsoft Fabric. This includes conceptual, logical and physical modelling for new data sources, business domains and reporting use cases.

• A key part of the role will be to help define reusable datasets across the bronze, silver and gold layers. Bronze will largely reflect raw or source-aligned data. Silver should become the trusted, standardized and reusable business-aligned layer. Gold should support consumption through reporting, semantic models, dashboards, analytics and future AI use cases.

• The role will work closely with Data Engineers to define source-to-target mappings, transformation rules, keys, relationships, data quality checks and history handling. It will also involve working with Analytics Engineers and Power BI teams to make sure downstream semantic models are built on consistent and well-understood data structures.

• The candidate will also support the definition of common enterprise entities, such as customer, product, supplier, location, transaction, order, contract, employee or other client-specific business concepts. The exact domains will depend on the systems being onboarded, but the principle is the same: create models that are clear, reusable and aligned to business meaning.

Key Responsibilities

● Design conceptual, logical and physical data models for enterprise data onboarding and analytics use cases.

● Define modelling patterns for Fabric Lakehouse, Fabric Warehouse and Power BI semantic consumption.

● Support the implementation of bronze, silver and gold data layers using Medallion Architecture principles.

● Design conformed dimensions, fact tables, reference data structures, master data views and analytics-ready datasets.

● Define model grain, business keys, surrogate keys, relationships, hierarchies and history handling.

● Create source-to-target mappings and work with engineers to turn modelling designs into working data assets.

● Help define reusable Silver-layer datasets that are more than cleansed copies of source systems.

● Design Gold-layer models around reporting, KPIs, business questions and decision making needs.

● Work with domain teams to understand business processes, data ownership, key metrics and analytical requirements.

● Support Power BI semantic model design by ensuring data structures are clear, performant and business-friendly.

● Document business definitions, model assumptions, lineage, data quality rules and known limitations.

● Work with governance teams to align models with naming standards, glossary terms, metadata and access requirements.

● Participate in architecture reviews, data model reviews and discussions around shared enterprise definitions.

● Help reduce duplicated reporting datasets and inconsistent KPI logic across the organization.

Expected Outputs

The role is expected to produce practical modelling artefacts that can be used by engineers, analysts, architects and business teams.
Typical outputs include:

● Conceptual and logical data models.

● Physical model designs for Fabric Lakehouse and Warehouse.

● Entity relationship diagrams.

● Dimensional models with facts, dimensions and defined grain.

● Source-to-target mapping…