Data Architect- MS Fabric

Role Summary:

The Fabric Data Architect is responsible for designing, governing, and implementing enterprise-scale data platforms using Microsoft Fabric. The role focuses on data architecture, Lakehouse design, data integration, governance, security, analytics enablement, and modernization of legacy data platforms. The architect collaborates with business stakeholders, solution architects, data engineers, and analytics teams to deliver scalable, secure, and high-performance data solutions.

Key Responsibilities:

Data Architecture & Platform Design

· Design enterprise data architecture using Microsoft Fabric components including OneLake, Lakehouse, Data Warehouse, Data Factory, Real-Time Intelligence, Semantic Models, and Power BI.

· Define conceptual, logical, and physical data models.

· Establish Lakehouse and Medallion Architecture patterns (Bronze, Silver, Gold).

· Design enterprise-wide data integration frameworks and reusable architecture patterns.

Data Engineering & Integration

· Architect batch, streaming, and event-driven ingestion solutions.

· Design data pipelines using Fabric Data Factory, Notebooks, Dataflows Gen2, and APIs.

· Integrate data from ERP, CRM, SaaS, databases, files, and external platforms.

· Implement scalable ELT/ETL solutions.

Data Modeling & Analytics

· Define dimensional models, semantic models, and data product structures.

· Design Direct Lake and Warehouse optimization strategies.

· Enable self-service BI and advanced analytics through Power BI.

Governance & Security

· Implement data governance using Microsoft Purview and Fabric governance capabilities.

· Define security architecture including RBAC, row-level security, masking, encryption, and access controls.

· Ensure compliance with privacy, regulatory, and organizational security standards.

· Establish data quality, metadata, lineage, and catalog strategies.

Migration & Modernization

· Lead migrations from legacy Data Warehouses, Synapse, Hadoop, SQL Server, Teradata, Snowflake, or Databricks to Microsoft Fabric.

· Define migration roadmaps and modernization strategies.

· Conduct platform assessments and architecture reviews.

Performance & Optimization

· Optimize Fabric workloads for scalability and performance.

· Monitor and improve query performance, storage utilization, and cost efficiency.

· Develop FinOps and capacity management strategies for Fabric F-SKUs.

Leadership & Stakeholder Management

· Conduct architecture workshops and client discussions.

· Provide technical leadership to data engineers, analysts, and BI teams.

· Review solution designs and ensure alignment with enterprise architecture standards.

· Support pre-sales, solutioning, estimations, and RFP/RFT responses.

Required Technical Skills:

Microsoft Fabric

· OneLake

· Lakehouse

· Data Warehouse

· Fabric Data Factory

· Real-Time Intelligence

· Eventstreams

· Dataflows Gen2

· Notebooks

· Semantic Models

· Direct Lake

· Power BI

· Fabric Administration & Governance

Data Engineering

· PySpark

· Spark SQL

· Python

· SQL

· Delta Lake

· Data Modeling

· ETL/ELT Design

Cloud & Integration

· Azure Data Lake Storage (ADLS Gen2)

· Azure Synapse Analytics

· Azure Data Factory

· Azure SQL

· Azure Event Hub

· Azure Functions

· REST APIs

Governance & Security

· Microsoft Purview

· Data Catalog

· Data Lineage

· Metadata Management

· Data Quality

· RBAC

· Data Security & Compliance

DevOps

· Azure DevOps

· Git Integration

· CI/CD Pipelines

· Infrastructure as Code

Preferred Certifications:

· DP-600: Implementing Analytics Solutions Using Microsoft Fabric

· DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric

· DP-203: Azure Data Engineer Associate

· Microsoft Certified: Azure Solutions Architect Expert

· Azure Fundamentals (AZ-900)

Key Competencies:

· Enterprise Data Architecture

· Modern Data Platform Design

· Data Governance

· Stakeholder Management

· Solution Consulting

· Technical Leadership

· Cost Optimization

· Architecture Review & Governance

Nice-to-Have:

· Databricks

· Snowflake

· Synapse Analytics

· AI/ML Integration

· Generative AI and Copilot Integration

· Data Mesh and Data Products Architecture