Fabric Data Lead/Architect
Fabric Data Lead/Architect (Day1 Onsite) Arlington, VA (Priority 1) and St. Louis, MO (Priority 2)
Experience 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
- 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
- Strong documentation discipline (blueprints, playbooks, reference patterns)
Behavioral Competencies Strong architectural thinking with a platform engineering mindset
Excellent stakeholder management and communication (technical + executive)
Ability to define standards and drive adoption across teams
Pragmatic approach—balances governance with agility and self-service
Strong documentation discipline (blueprints, playbooks, reference patterns)