Microsoft Fabric SME

Avanade
Bengaluru, Gurugram
Workplace: OnsiteFull timeFunction: Data Analytics & Business IntelligenceExperience: 8-12 yearsSkills: ["Technical ownership","Problem-solving","Stakeholder collaboration","Troubleshooting","Mentoring"]

Serve as a hands-on Microsoft Fabric technical specialist for modern data estate builds. Design and implement OneLake, lakehouse/warehouse components, Fabric Data Factory pipelines/dataflows, real-time intelligence, and Power BI semantic/reporting layers. Execute data migration workstreams from legacy on-prem and cloud platforms to Fabric, resolve complex implementation edge cases, and support post–go-live stabilization with governance and security via Purview and lineage.

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Avanade
Avanade
4 hours ago

Microsoft Fabric SME

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Last checked: 4 hours agoStatus: Live

Job Summary

Serve as a hands-on Microsoft Fabric technical specialist for modern data estate builds. Design and implement OneLake, lakehouse/warehouse components, Fabric Data Factory pipelines/dataflows, real-time intelligence, and Power BI semantic/reporting layers. Execute data migration workstreams from legacy on-prem and cloud platforms to Fabric, resolve complex implementation edge cases, and support post–go-live stabilization with governance and security via Purview and lineage.
Location: Bengaluru, Gurugram
Workplace: Onsite
Employment Type: Full time
Job Function: Data Analytics & Business Intelligence
Seniority: Mid level

Key Responsibilities

  • •Design and build Microsoft Fabric solution components including OneLake, lakehouse/warehouse, Fabric pipelines/dataflows, real-time intelligence, and Power BI semantic/reporting layers.
  • •Implement ingestion, transformation, and modeling patterns for data lake, data warehouse, and lakehouse architectures.
  • •Act as the Microsoft Fabric go-to technical specialist on project teams, resolving complex implementation issues and edge cases.
  • •Execute data migration workstreams moving data/workloads from legacy platforms (on-prem warehouses, Synapse, Databricks, Snowflake, or other cloud data platforms) to Fabric.
  • •Write and review production-quality pipeline code/configurations, ensuring engineering best practices, testing, and CI/CD; support post–go-live stabilization and troubleshooting.

Key Requirements

  • •8–12 years in data engineering with strong recent hands-on experience building solutions on Microsoft Fabric.
  • •Hands-on expertise across OneLake, lakehouse/warehouse, Fabric Data Factory pipelines/dataflows, notebooks (Spark), and Power BI integration.
  • •Strong knowledge of modern data estate patterns (data lake/warehouse, medallion architecture, dimensional modeling).
  • •Experience running data migration workstreams moving legacy data/workloads to Fabric (including legacy on-prem or cloud platforms).
  • •Working knowledge of the Azure data ecosystem (Synapse, Azure Data Factory, Databricks, ADLS Gen2, Azure SQL) plus strong SQL, data modeling, and performance tuning.
Experience:8-12 yearsData engineeringData platformsData lakehouseData migrationMicrosoft Azure
Skills:Technical ownershipProblem-solvingStakeholder collaborationTroubleshootingMentoring
Certifications:Fabric Analytics Engineer Associate (DP-600)Azure Data Engineer Associate (DP-203)Azure Data Engineer Associate (DP-700)
Tech Stack:Microsoft FabricOneLakeLakehouseData WarehouseFabric Data FactoryDataflowsReal-Time IntelligencePower BISQLSparkPySparkMicrosoft PurviewFabric workspace governanceData lineageAzure Synapse AnalyticsAzure Data FactoryAzure DatabricksADLS Gen2Azure SQLAzure DevOps

Company Brief

Avanade
Global digital innovation and IT services company delivering Microsoft-based cloud, digital and application services. Partners with enterprises to design, build and run business solutions across cloud, data, AI, modern workplace and industry-specific transformations.
Industry: Consulting
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Headquarters: Seattle, United States
Founded: 2000
Glassdoor
Glassdoor: 3.9
WebsiteLinkedIn