数据架构师 - Microsoft Fabric

Position Summary
We are seeking a highly skilled and hands-on Senior Data Architect to lead the design, implementation, and governance of enterprise-scale data platforms built on Microsoft Fabric. This role is responsible for architecting modern data solutions, defining Fabric landing zones, establishing enterprise data standards, and leading the technical delivery of scalable, secure, and AI-ready analytics platforms.
The ideal candidate brings deep expertise in Microsoft Fabric, Azure data services, data architecture, governance, DevOps, and enterprise integration patterns. This is a technical leadership role requiring strategic architecture experience and hands-on implementation capability.
Key Responsibilities
Microsoft Fabric Architecture & Platform Design

• Design and implement enterprise-scale Microsoft Fabric solutions across OneLake, Lakehouse, Data Warehouse, Data Factory, Real-Time Intelligence, Power BI, notebooks, and Spark.

• Define enterprise data-platform standards, reference architectures, reusable patterns, and workload boundaries.

• Design and optimize Direct Lake, Import, and Composite semantic model architectures.

• Establish scalable, multi-domain data architectures for enterprise analytics, artificial intelligence, and machine learning workloads.

Fabric Landing Zone Design

• Lead the design and implementation of Microsoft Fabric landing zones.

• Define tenant architecture, workspace topology, domain strategy, capacity planning, environment separation, naming standards, and security boundaries.

• Design role-based access control, Microsoft Entra ID integration, workspace governance, sensitivity labels, data-protection controls, and Microsoft Purview integration.

• Develop landing-zone blueprints that support scalability, compliance, observability, operational excellence, and repeatable onboarding of business domains.

• Define connectivity and network-integration patterns, including gateways, private connectivity considerations, managed identities, secrets management, and hybrid data access.

Data Engineering & Integration Architecture

• Architect ingestion and transformation frameworks for ERP, CRM, SaaS, legacy databases, APIs, files, and event streams.

• Design Bronze, Silver, and Gold medallion patterns using Fabric Lakehouse and Warehouse capabilities.

• Establish standards for data quality, lineage, transformation, metadata, master data management, schema evolution, and source-to-target mapping.

• Define batch, near-real-time, and streaming integration patterns while ensuring end-to-end observability and supportability.

Data Governance & Security

• Establish governance frameworks using Microsoft Fabric and Microsoft Purview.

• Define policies for metadata management, lineage, classification, stewardship, retention, access, auditability, privacy, and compliance.

• Design secure architectures using Microsoft Entra ID, MFA, Conditional Access, private endpoints where applicable, Azure Key Vault, managed identities, and Privileged Identity Management.

• Ensure solution designs align with enterprise security and regulatory requirements.

Semantic Modeling & Analytics Architecture

• Design scalable semantic models for enterprise reporting, self-service analytics, and AI workloads.

• Establish conformed dimensions, KPI frameworks, reusable measures, and standardized business definitions.

• Guide Power BI architecture, performance optimization, security, deployment, and governance.

• Enable governed self-service analytics through certified and reusable semantic models.

AI & Large Language Model Solution Architecture

• Architect secure, scalable AI and large language model solutions that leverage enterprise data and integrate with platforms such as Azure OpenAI and Microsoft Fabric.

• Design retrieval-augmented generation, semantic search, vectorization, prompt orchestration, model-serving, and agent-based solution patterns.

• Define data, integration, API, identity, networking, and observability architectures required to operationalize and serve AI capabilities across enterprise applications.

• Establish responsible AI standards covering security, privacy, grounding, content safety, evaluation, monitoring, cost management, and human oversight.

• Partner with data science, engineering, security, and business teams to evaluate AI use cases and translate them into production-ready architectures.

Dev Ops, Automation & Operations

• Implement Fabric DevOps practices using Azure DevOps, GitHub Enterprise, Fabric deployment pipelines, source control, CI/CD automation, and Infrastructure as Code.

• Define release management, branching, testing, approval, rollback, and environment-promotion strategies.

• Establish monitoring, logging, capacity management, cost management, incident response, and platform-support models.

• Automate deployments using Terraform, Bicep, YAML pipelines, fabric-cicd, APIs, and Fabric-native capabilities as appropriate.

Technical Leadership

• Serve as the technical authority for enterprise data architecture initiatives and architecture review boards.

• Mentor Data Engineers, Data Analysts, BI Developers, and Solution Architects.

• Lead discovery workshops, technical design sessions, proofs of concept, and client-facing architecture reviews.

• Translate business and nonfunctional requirements into actionable platform and solution designs.

Required Qualifications

• 10+ years of experience in data architecture, data engineering, business intelligence, or enterprise analytics.

• 5+ years designing Azure-based enterprise data platforms.

• Demonstrated hands-on experience designing and implementing Microsoft Fabric solutions.

• Deep expertise in OneLake, Fabric Lakehouse, Fabric Data Warehouse, Data Factory, Spark notebooks, Power BI, semantic models, and Direct Lake.

• Strong understanding of medallion architecture, dimensional modeling, data vault concepts, lakehouse design, and distributed data-processing patterns.

• Experience designing enterprise landing zones, tenant and workspace strategies, governance models, and security frameworks.

• Expertise with Microsoft Entra ID, RBAC, Microsoft Purview, Azure Key Vault, managed identities, Conditional Access, and PIM.

• Experience with Git, Azure DevOps or GitHub, CI/CD, automated testing, deployment pipelines, and Infrastructure as Code.

• Advanced SQL skills plus hands-on Python and PySpark development experience.

• Experience integrating data across ERP, CRM, SaaS, APIs, event streams, and on-premises systems.

• Ability to create architecture diagrams, logical and physical data models, decision records, standards, and detailed solution-design documentation.

Preferred Qualifications

• Microsoft Certified: Fabric Analytics Engineer Associate or Fabric Data Engineer Associate.

• Microsoft Certified: Azure Solutions Architect Expert or Azure Data Engineer Associate.

• Experience with Dynamics 365, Salesforce, SAP, Oracle, Databricks, Profisee MDM, Fivetran, or equivalent enterprise platforms.

• Experience with KQL, Eventstreams, Real-Time Intelligence, machine learning, Copilot, or generative AI solution patterns.

• Experience supporting architecture assessments, cloud-adoption frameworks, regulated environments, or Microsoft specialization audits.

Technical Skills
CategoryCore Technologies & CapabilitiesFabric Platform
OneLake; Lakehouse; Warehouse; Data Factory; notebooks; Spark; Real-Time Intelligence; capacities; tenant and workspace administration
Analytics
Power BI; semantic models; Direct Lake; DAX; dimensional modeling; performance optimization; row-level and object-level security
Engineering
SQL; Python; PySpark; Delta; pipelines; dataflows; APIs; batch and streaming integration; data quality and observability
Landing Zone & Security
Tenant and workspace strategy; Entra ID; RBAC; Purview; Key Vault; managed identities; Conditional Access; PIM; governance and compliance
DevOps & Automation
Git; Azure DevOps; GitHub; Fabric deployment pipelines; CI/CD; Terraform; Bicep; YAML; automated testing and release controls
AI & Large Language Models
Azure OpenAI; generative AI; LLM architecture; retrieval-augmented generation (RAG); embeddings and vector search; semantic search; prompt and agent orchestration; APIs and model serving; responsible AI; content safety; evaluation, monitoring, and cost controls
Success Measures

• Successful design and deployment of secure, scalable Microsoft Fabric landing zones.

• Consistent adoption of platform, governance, security, and engineering standards across data domains.

• Delivery of reliable, performant, reusable data products and semantic models.

• Improved data quality, lineage visibility, operational monitoring, and audit readiness.

• Reduced reporting latency and platform-administration overhead through automation and standardization.

• Establishment of a governed, AI-ready enterprise data foundation.

Candidate Profile
This role is best suited for a senior technical architect who can move comfortably between enterprise strategy, detailed solution design, and hands-on implementation. The successful candidate will communicate effectively with executives, business stakeholders, security teams, platform administrators, and engineering teams while maintaining strong ownership of architecture quality and delivery outcomes.
Originally posted on Himalayas