Fabric Data Engineer​/Lead

Fabric Data Engineer/Lead

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

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 One Lake 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
  -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 Dev Ops Automation: CI/CD (Git workflows, release/promotion strategies) Scripting/automation mindset (Power Shell/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 (One Lake 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)