Fabric Data Architect/Lead

Fabric Data Lead/Architect (Day1 Onsite)Arlington, VA (Priority 1) and St. Louis, MO (Priority 2)Experience8–12+ years in Data Architecture / Analytics Platforms / Cloud Data Engineering2–4+ 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 scaleKey Responsibilities (Must-Have)Fabric Platform DesignWorkspace Architecture: Design scalable workspace and capacity strategy:Domain-aligned and environment-separated structure (dev/test/prod)Naming conventions, tagging/taxonomy, ownership modelDesign OneLake organization:Folder conventions, zones (landing/curated/serving), lifecycle conventionsStandards for Delta table structure, partitioning, retention, and schema evolutionDefine item and data product blueprints: When to use Lakehouse vs Warehouse vs Real-time capabilitiesHow to structure pipelines, notebooks, dataflows, and semantic modelsDefine and implement architecture patterns:Medallion architecture standards and curated modeling approachDimensional modeling strategy for data martsSemantic model standards for reuse, performance, and governanceSecurityidentity Setup:Microsoft Entra ID group-based RBACLeast privilege patterns, separation of dutiesRLS/OLS patterns in semantic modelsDesign and Setup Governance, including but not limited to: Apply Fabric-native governance best practices:Workspace roles and permission bundles for personasControlled sharing patterns to reduce data sprawlStandards for certification/endorsement processWork with governance teams to ensure:Metadata capture conventions are consistently appliedData Lineage is capturedSensitivity labeling strategy is embedded in workflowsBuild Frameworks around DevOpsAutomation: CI/CD (Git workflows, release/promotion strategies)Scripting/automation mindset (PowerShell/Python preferred; REST APIs)Monitoring, ObservabilityOperational Readiness:Design and implement monitoring for:Pipelines, notebooks, dataflows execution success and runtimesWarehouse/Lakehouse query performance and refresh healthSemantic model refresh and usage trendsCapacity utilization and throttling patternsDefine alerting thresholds, incident classification, and runbooksDrive operational readiness gates before production cutoversCost Optimization: Implement design-time and run-time cost optimization:Scheduling and workload shaping to reduce peak contentionReuse 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 workloadsCost allocation approach by workspace/domain where feasibleEnablement, Standards, and Collaboration with Delivery TeamsDefine “golden path” patterns and accelerate delivery: Templates and standards for pipelines and lakehouse layoutPR review checklists for Fabric engineering deliverablesProvide architecture oversight during implementation:Design reviews, technical governance checkpoints, risk mitigationCoach teams on best practices:Performance, security, operational readiness, and governance adoptionBehavioral CompetenciesStrong architectural thinking with a platform engineering mindsetExcellent stakeholder management and communication (technical + executive)Ability to define standards and drive adoption across teamsPragmatic approach—balances governance with agility and self-serviceStrong documentation discipline (blueprints, playbooks, reference patterns)