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)