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.
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.
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.
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.
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 slowly changing dimensions.
Strong SQL is required. The person does not need to be a full-time data engineer, but they should be able to read transformation logic, understand joins and aggregations, and challenge whether the implemented logic matches the intended business model.
The candidate should understand modern cloud data platforms and Lakehouse concepts. Experience with Microsoft Fabric is strongly preferred, especially Fabric Lakehouse, Fabric Warehouse, OneLake and Power BI semantic models.
They should also understand the practical role of governance in data modelling: naming standards, definitions, ownership, lineage, quality expectations, access controls and metadata.
Enterprise data modelling and data warehousing.
Conceptual, logical and physical data modelling.
Dimensional modelling, including facts, dimensions, star schema and conformed dimensions.
Designing analytics-ready datasets for BI and reporting.
Strong SQL.
Experience with cloud data platforms or modern data lake/lakehouse architectures.
Understanding of Bronze, Silver and Gold data layers.
Working with architects, data engineers, analysts and business stakeholders.
Documenting data definitions, mappings, lineage and model assumptions.
Microsoft Fabric.
Fabric Lakehouse and Fabric Warehouse.
OneLake.
Power BI semantic models.
Delta Lake.
Microsoft Purview.
Azure data services.
Data product-oriented delivery.
Data quality and metadata management.
Agile delivery environments.
Why join us?
ü Work with a passionate and innovative team in a fast-paced, growth-oriented environment.
ü Gain hands-on experience in content marketing with exposure to real-world projects.
ü Opportunity to learn from experienced professionals and enhance your marketing skills.
ü Contribute to exciting initiatives and make an impact from day one.
ü Competitive stipend and potential for growth within the company.
ü Recognized for excellence in data and AI solutions with industry awards and accolades.
Employee Benefits
1. Culture:
a. Open Door Policy: Encourages open communication and accessibility to management.
b. Open Office Floor Plan: Fosters a collaborative and interactive work environment.
c. Flexible Working Hours: Allows employees to have flexibility in their work schedules.
d. Employee Referral Bonus: Rewards employees for referring qualified candidates.
e. Appraisal Process Twice a Year: Provides regular performance evaluations and feedback.
2. Inclusivity and Diversity:
a. Hiring practices that promote diversity: Ensures a diverse and inclusive workforce.
b. Mandatory POSH training: Promotes a safe and respectful work environment.
3. Health Insurance and Wellness Benefits:
a. GMC and Term Insurance: Offers medical coverage and financial protection.
b. Health Insurance: Provides coverage for medical expenses.
c. Disability Insurance: Offers financial support in case of disability.
4. Child Care & Parental Leave Benefits:
a. Company-sponsored family events: Creates opportunities for employees and their families to bond.
b. Generous Parental Leave: Allows parents to take time off after the birth or adoption of a child.
c. Family Medical Leave: Offers leave for employees to take care of family members' medical needs.
5. Perks and Time-Off Benefits:
a. Company-sponsored outings: Organizes recreational activities for employees.
b. Gratuity: Provides a monetary benefit as a token of appreciation.
c. Provident Fund: Helps employees save for retirement.
d. Generous PTO: Offers more than the industry standard for paid time off.
e. Paid sick days: Allows employees to take paid time off when they are unwell.
f. Paid holidays: Gives employees paid time off for designated holidays.
g. Bereavement Leave: Provides time off for employees to grieve the loss of a loved one.
6. Professional Development Benefits:
a. L&D with FLEX- Enterprise Learning Repository: Provides access to a learning repository for professional development.
b. Job Training: Provides training to enhance job-related skills.
c. Professional Certification Reimbursements: Assists employees in obtaining professional certifications.
d. Promote from Within: Encourages internal growth and advancement opportunities.
