Data Architecture

• 8–12+ years in Data Architecture / Analytics Platforms / Cloud Data Engineering
• 2–4+ years in Microsoft analytics ecosystem (Fabric / Power BI / Synapse / Azure Data)
• Proven experience designing platforms for large enterprises (multi-team, multi-domain, 1k+ users)
• Experience implementing governance and security at scale

Key Responsibilities (Must-Have)

• Fabric Platform Design & Workspace Architecture:
  • Design scalable workspace and capacity strategy:
    • Domain-aligned and environment-separated structure (dev/test/prod)
    • Naming conventions, tagging/taxonomy, ownership model
  • Design OneLake organization:
    • Folder conventions, zones (landing/curated/serving), lifecycle conventions
    • Standards for Delta table structure, partitioning, retention, and schema evolution
  • Define item and data product blueprints:
    • When to use Lakehouse vs Warehouse vs Real-time capabilities
    • How to structure pipelines, notebooks, dataflows, and semantic models
  • Define and implement architecture patterns:
    • Medallion architecture standards and curated modeling approach
    • Dimensional modeling strategy for data marts
    • Semantic model standards for reuse, performance, and governance
• Security & identity Setup:
  • Microsoft Entra ID group-based RBAC
  • Least privilege patterns, separation of duties
  • RLS/OLS patterns in semantic models
• Design and Setup Governance, including but not limited to:
  •  Apply Fabric-native governance best practices:
    • Workspace roles and permission bundles for personas
    • Controlled sharing patterns to reduce data sprawl
    • Standards for certification/endorsement process
  • Work with governance teams to ensure:
    • Metadata capture conventions are consistently applied
    • Data Lineage is captured
    • Sensitivity labeling strategy is embedded in workflows
• Build Frameworks around DevOps & Automation:
  • CI/CD (Git workflows, release/promotion strategies)
  • Scripting/automation mindset (PowerShell/Python preferred; REST APIs)
• Monitoring, Observability & Operational Readiness:
  • Design and implement monitoring for:
    • Pipelines, notebooks, dataflows execution success and runtimes
    • Warehouse/Lakehouse query performance and refresh health
    • Semantic model refresh and usage trends
    • Capacity utilization and throttling patterns
  • Define alerting thresholds, incident classification, and runbooks
  • Drive operational readiness gates before production cutovers
• Cost Optimization:
  • Implement design-time and run-time cost optimization:
    • Scheduling and workload shaping to reduce peak contention
    • Reuse strategies (shared curated layers, shared semantic models)
    • Identify duplication and encourage governed reuse (OneLake alignment)
  • Provide capacity strategy inputs:
    • Right-sizing, workload isolation guidance for critical workloads
    • Cost allocation approach by workspace/domain where feasible
• Enablement, Standards, and Collaboration with Delivery Teams
  • Define “golden path” patterns and accelerate delivery:
    • Templates and standards for pipelines and lakehouse layout
    • PR review checklists for Fabric engineering deliverables
  • Provide architecture oversight during implementation:
    • Design reviews, technical governance checkpoints, risk mitigation
  • Coach teams on best practices:
    • Performance, security, operational readiness, and governance adoption