Fabric Data Engineer/Lead (Day1 Onsite)

Experience

• 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)