Fabric Data Engineer/Lead

Design and Setup Governance, including but not limited to: Apply Fabric-native governance best practices: Workspace roles and permission bundles for personas. Workspace Architecture: Design scalable workspace and capacity strategy: Domain-aligned and environment-separated structure (dev/test/prod).

Fabric Data Engineer/Lead (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, 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
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)