Microsoft Fabric Architect/Lead

About the role

We are seeking an experienced Microsoft Fabric Architect to lead the architecture, design and delivery of a secure, scalable enterprise data platform for a regulated critical infrastructure environment. The role combines strategic architecture leadership with hands‑on design across Microsoft Fabric, OneLake, medallion architecture, metadata‑driven ingestion, data modelling, orchestration, capacity management and Power BI.

Key responsibilities

• Own the end‑to‑end Microsoft Fabric architecture, target state, standards, guardrails and design governance.
• Design OneLake, workspace and environment structures across Lakehouse, Warehouse, Data Factory, Notebooks, Semantic Models and Power BI.
• Define Bronze, Silver and Gold data layers, enterprise data models and consumption patterns, including Direct Lake, Import and DirectQuery.
• Lead the design of a reusable metadata‑driven ingestion framework for batch, incremental, change data capture and streaming workloads.
• Review all deliverables – code, design documents and supporting tracks such as data governance, Azure foundations, networking, security and DevOps.
• Establish orchestration, data quality, observability, restartability, quarantine and remediation patterns for configuration‑led source onboarding.
• Size and optimise Fabric capacities for performance, workload isolation, scalability and cost across development, test and production environments.
• Set SQL, PySpark, Delta Lake and notebook engineering standards and provide technical assurance through architecture and code reviews.

Required profile

• Proven experience designing and delivering enterprise‑scale data platforms using Microsoft Fabric and related services.
• Strong ability to work hands‑on with Microsoft Fabric while guiding data development teams.
• Experience collaborating with Azure platform, security, networking, DevOps and data governance teams.

Required skills

• Microsoft Fabric components: OneLake, Lakehouse, Warehouse, Data Factory, Notebooks, Semantic Models, Power BI.
• Capacity management, performance tuning and cost optimisation.
• Medallion architecture and metadata‑driven ingestion.
• Data modelling: dimensional modelling, Data Vault, enterprise information models.
• Orchestration: Data Factory pipelines, notebook orchestration, dependency management.
• SQL, PySpark, Delta Lake.