Senior Microsoft Fabric Performance & Scalability Specialist

Location: Noida, India

Department: PE-Cloud

Experience: 10+

Skills: ADLS Gen2, Azure Data Factory, Databricks, Fabric

• Optimize Microsoft Fabric performance, scalability, concurrency, and capacity utilization across Fabric Data Warehouse, Lakehouse, OneLake, Shortcuts, Pipelines, and Notebooks.
• Design and enhance the platform to efficiently support migration and ongoing processing for 1,500+ tenants/clients.
• Develop scalable processing strategies for high-volume and low-volume tenants while meeting data-load and reporting SLAs.
• Analyze Fabric capacity/CU utilization, throttling, concurrency, Query Insights, and workload performance.
• Implement optimization recommendations to improve performance, scalability, and resource utilization.
• Optimize tenant grouping, batching, parallel processing, concurrency control, workload prioritization, watermark/batch processing, retries, and failure recovery.
• Optimize Fabric Data Warehouse SQL queries, data loads, joins, table structures, and CLUSTER BY strategies.
• Conduct performance, scalability, concurrency, stress, and load testing and establish measurable performance benchmarks.

• Develop and enhance Fabric notebooks using PySpark, Python, and SQL for scalable full and incremental processing.
• Build reusable and scalable processing frameworks for multi-tenant workloads.
• Develop and optimize Fabric pipelines and data-processing workflows.
• Implement reliable failure recovery, retry, batching, and incremental-processing mechanisms.
• Ensure development follows scalable, maintainable, and production-ready engineering practices.

• Work closely with the ETL/Data Engineering team to onboard new data sources and new sets of tables into Microsoft Fabric.
• Analyze new source schemas, tables, relationships, data volumes, dependencies, primary keys, and incremental-load requirements.
• Perform required development, code changes, configuration, and testing in Microsoft Fabric to support newly introduced data sources and tables.
• Develop and enhance Fabric notebooks, pipelines, SQL code, metadata/control frameworks, Lakehouse objects, and Fabric Data Warehouse tables required for new data sources.
• Configure and test full and incremental data-loading processes for newly onboarded tables.
• Perform unit, integration, data-validation, and performance testing before production deployment.
• Coordinate with ETL teams to validate source-to-target mappings, transformation logic, dependencies, and data completeness.
• Integrate new data sources into the established Bronze → Silver → Gold → Lakehouse/Fabric Data Warehouse architecture.
• Develop reusable and metadata-driven patterns to reduce development effort when onboarding future data sources and tables.

• Work directly with clients and internal teams during onboarding, migration, production cutover, and post-production support.
• Understand client data volumes, processing requirements, dependencies, and SLA expectations.
• Support client onboarding including full loads, incremental-load enablement, data validation, performance validation, and production cutover.
• Investigate and resolve client-reported data, performance, data-load, and reporting issues.
• Perform end-to-end root-cause analysis across source systems, Medallion layers, Fabric notebooks, pipelines, Lakehouse, Data Warehouse, and reporting workloads.
• Troubleshoot missing or duplicate data, failed or delayed loads, incremental-processing issues, performance degradation, and SLA violations.
• Implement permanent fixes and enhance shared frameworks to prevent recurring issues across tenants.

• Monitor production workloads and ensure data-load and reporting SLAs are consistently achieved.
• Identify performance bottlenecks, capacity constraints, concurrency issues, and processing failures.
• Prioritize production issues based on business impact and SLA requirements.
• Coordinate with ETL, Data Engineering, reporting, and client-facing teams to resolve complex production issues.
• Document root-cause analysis, corrective actions, and preventive measures.
• Drive continuous improvements to platform reliability, performance, and scalability.

• 10+ years of experience in Data Engineering, Data Warehousing, ETL/ELT, or enterprise data platforms.
• Strong recent hands-on Microsoft Fabric production experience.
• Proven experience in Microsoft Fabric performance optimization and scalability.
• Strong hands-on experience with Fabric Data Warehouse, Lakehouse, OneLake, Pipelines, and Notebooks.
• Advanced PySpark, Python, and SQL development and performance-tuning skills.
• Strong production experience with Medallion Architecture (Bronze/Silver/Gold) – mandatory.
• Experience developing and optimizing large-scale full and incremental data-loading frameworks.
• Experience handling high-volume, concurrent, and multi-tenant workloads.
• Strong understanding of Fabric capacity, CU utilization, concurrency, throttling, and workload management.
• Hands-on experience with new data-source/table onboarding, development, testing, and production deployment.
• Experience working collaboratively with ETL/Data Engineering teams to implement source-to-target data pipelines.
• Experience working directly with clients during onboarding, migration, and production issue resolution.
• Strong troubleshooting, root-cause analysis, communication, and problem-solving skills.

• Microsoft Fabric.
• Fabric Data Warehouse.
• Microsoft Fabric Lakehouse.
• OneLake.
• Fabric Pipelines.
• Fabric Notebooks.
• PySpark.
• Python.
• Advanced SQL.
• SQL Performance Tuning.
• Medallion Architecture – Bronze/Silver/Gold.
• Full and Incremental Data Processing.
• Multi-Tenant Data Processing.
• Fabric Capacity and CU Optimization.
• Concurrency and Workload Management.
• Data Source and Table Onboarding.
• Production Support and Troubleshooting.
• SLA Management.

• Experience supporting platforms with 1,000+ tenants/clients or comparable enterprise-scale workloads.
• Fabric F-SKU capacity optimization and workload management.
• ADLS Gen2.
• Azure Data Factory.
• Databricks.
• Delta Lake.
• OneLake Shortcuts.
• Metadata-driven ETL/ELT frameworks.
• Large-scale data migration.
• Load and concurrency testing.
• High-concurrency reporting workloads.

• Strong hands-on development and performance optimization mindset.
• Ability to take a new data source or set of tables from analysis through Fabric development, testing, deployment, and production support.
• Strong production troubleshooting and root-cause analysis capabilities.
• Ability to work effectively with ETL, Data Engineering, reporting, and client-facing teams.
• Strong understanding of enterprise-scale and multi-tenant data processing.
• Excellent communication and stakeholder management skills.
• Strong ownership of production stability, performance, and SLA adherence.
• Ability to work effectively in a high-volume, fast-paced production environment.
• Strong analytical and problem-solving capabilities.
• Proactive approach to performance optimization and continuous improvement.