01Overview
We are looking for a Senior Data Modeller
to support a Microsoft Fabric data platform programme for a large enterprise
client.
We are building a more
product-oriented data organisation, where trusted data assets are created once,
governed properly and reused across business domains. 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 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,
standardised 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 explicit, 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.
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
documents.
Data product or dataset
specifications.
Data dictionaries and business
definitions.
Lineage and dependency
documentation.
Data quality rule definitions.
Naming standards and modelling
design patterns.
Inputs into Power BI semantic
model design.
Model review packs for
architecture or governance forums.
Requirements
Required
Skills
The candidate should have strong hands-on
experience in enterprise data modelling and data warehousing. They should be
confident with dimensional modelling, including star schemas, facts,
dimensions, conformed dimensions and .
