About the Role
We are looking for an experienced Microsoft Fabric Data Architect around 8+ years to define the target-state architecture and help shape the future of an enterprise data platform. The platform foundation includes Microsoft Fabric, OneLake, Lakehouses, Data Pipelines, Power BI, and a Medallion Architecture. The role will strengthen alignment across architecture, governance, reporting, security, and operating processes so the platform delivers trusted data, consistent insights, and measurable business value.
Key Responsibilities
- Define and own the target-state Microsoft Fabric architecture and supporting technology roadmap.
- Establish architecture standards across OneLake, Lakehouses, Data Warehouses, Data Pipelines, Dataflows Gen2, Notebooks, Semantic Models, Power BI, PySpark, SQL, and Azure Data Factory.
- Define and govern Medallion Architecture patterns covering ingestion, transformation, data quality, and consumption across Bronze, Silver, and Gold layers.
- Provide clear architectural guidance on the adoption of Fabric-native capabilities versus existing Azure data services.
- Design a scalable, secure, maintainable, and future-ready enterprise data platform.
- Define and implement data ownership, governance, security, privacy, compliance, and data stewardship frameworks.
- Establish a trusted single source of truth through standardized data models, KPI definitions, and business metrics.
- Improve data quality, lineage, metadata management, and reporting consistency across the organization.
- Drive CI/CD, Git, release management, deployment automation, and environment-management best practices.
- Lead platform optimization covering performance, scalability, workload management, and capacity planning.
- Lead data platform modernization and migration initiatives from legacy or on-premises platforms to Microsoft Fabric and Azure.
- Collaborate with business, enterprise architecture, security, engineering, analytics, and reporting stakeholders to align platform investments with strategic objectives.
- Provide architectural leadership, guidance, and mentoring across data engineering, analytics, and reporting teams.
Required Skills and Experience
- Strong architecture experience with Microsoft Fabric and enterprise Azure data platforms.
- Expertise in OneLake, Lakehouse and Data Warehouse architecture, Data Pipelines, Dataflows Gen2, Notebooks, and Semantic Models.
- Strong knowledge of Medallion Architecture and enterprise data ingestion, transformation, modelling, and consumption patterns.
- Hands-on understanding of PySpark, Python, SQL, Spark SQL, and Azure Data Factory.
- Experience with Power BI architecture, semantic modelling, standardized KPI definitions, and reporting governance.
- Expertise in enterprise data modelling, integration patterns, APIs, and data platform modernization.
- Experience defining data governance, ownership, stewardship, security, privacy, GDPR, and compliance controls.
- Strong understanding of data quality, lineage, metadata management, and master or reference data concepts.
- Experience with Azure DevOps, Git, CI/CD, release management, and deployment automation.
- Experience in performance optimization, workload management, scalability, and capacity planning.
- Strong stakeholder management, communication, architecture documentation, and decision-making skills.
Preferred Skills
- Microsoft Purview and Microsoft Fabric governance experience.
- Experience establishing enterprise operating models for data platforms.
- Experience leading large-scale data transformation and modernization programmes.
- Relevant Microsoft Azure, Fabric, data architecture, or Power BI certifications.
Why This Role Matters
The Microsoft Fabric platform foundation is established, while further maturity is required across architecture choices, governance adoption, reporting consistency, data ownership, scalability, and operating processes. The Data Architect will address these areas by defining a clear roadmap, establishing architecture and governance standards, improving platform performance, standardizing data models and KPI definitions, and aligning business and technology teams around a common enterprise data vision.