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Azure Data Architect and Microsoft Fabric Architect with 10+ years of experience designing, architecting, developing, and modernizing enterprise-scale data platforms and analytics solutions across cloud and hybrid environments. Strong expertise in Microsoft Fabric, including OneLake, Lakehouse, Fabric Data Factory, Data Pipelines, Dataflows Gen2, Fabric Notebooks, Warehouses, Semantic Models, Power BI, and Fabric capacity management. Experienced in designing and implementing end-to-end Medallion Architecture using Bronze, Silver, and Gold layers, with a focus on scalable ingestion, incremental processing, transformation, data quality, governance, and performance optimization.

Hands-on experience building Fabric pipelines and PySpark notebooks to ingest and process data from on-premises SQL Server, Azure SQL, REST APIs, flat files, and other enterprise data sources. Proven experience in migrating and modernizing legacy Azure Data Factory, Synapse Analytics, and Databricks workloads to Microsoft Fabric, while establishing reusable data engineering frameworks and standardized development practices. Skilled in Azure Databricks, Delta Lake, Azure Data Factory, Synapse Analytics, ADLS Gen2, SQL, Python, and PySpark for large-scale data processing and transformation.

Experienced in implementing enterprise reporting solutions using Power BI and Fabric Semantic Models, including optimized DAX, Row-Level Security (RLS), incremental refresh, and performance tuning. Strong understanding of Microsoft Purview for data cataloging, governance, lineage, and data discovery. Hands-on experience implementing Git integration, Deployment Pipelines, CI/CD, environment-based deployments, and release management for Microsoft Fabric solutions.

Experienced in Fabric capacity optimization, workload management, partitioning, incremental processing, and performance tuning to deliver scalable and cost-effective analytics platforms. Strong understanding of enterprise data architecture, cloud data engineering, data governance, security, monitoring, data quality, and DevOps practices, with the ability to translate business requirements into robust, scalable, and maintainable data solutions.

GDF Development - BitWave Solutions (Walwil)

(2024-06 - 2026-06)

• Architected and implemented enterprise-scale Microsoft Fabric Lakehouse solutions using Medallion Architecture (Bronze, Silver, and Gold) on OneLake.
• Designed Fabric Data Pipelines to ingest data from on-premises SQL Server, Azure SQL and flat files.
• Developed Fabric Notebooks using PySpark for data transformation, enrichment, incremental processing, and data quality validation.
• Implemented Dataflow Gen2, Lakehouse, Warehouse, and Semantic Models to support enterprise analytics and reporting.
• Developed Power BI reports using Fabric Semantic Models, Row-Level Security (RLS), and optimized DAX.
• Configured Git integration, Deployment Pipelines, and CI/CD processes for automated Fabric deployments across environments.
• Integrated Microsoft Purview for data governance, cataloging, lineage, and enterprise data discovery.
• Optimized Fabric capacity, data partitioning, workload performance, and incremental data processing.
• Led the migration of legacy Azure Data Factory and Synapse workloads to Microsoft Fabric.

ELV Business Analytics Platform - Hydus Technologies (EMR Group)

(2022-04 - 2024-06)

• Led the development by leveraging Data Factory pipelines, Lakehouse, Fabric Notebooks.
• Architected Microsoft Fabric data platforms using Medallion Architecture across Bronze, Silver, Gold layers.
• Developed reusable PySpark transformation frameworks and implemented data quality, validation, monitoring processes.
• Implemented CI/CD and deployment strategies for Microsoft Fabric, integrating Git, Data Factory pipelines, Lakehouse.

Enterprise Data Platform Modernization - Photon Interactive (Amsive)

(2021-06 - 2021-12)

• Built reusable PySpark transformation frameworks on Azure Databricks to support scalable data ingestion, cleansing, transformation, enrichment, and incremental data processing across enterprise datasets.
• Implemented Delta Lake solutions on Azure Databricks and ADLS Gen2, leveraging Delta tables, MERGE operations, partitioning, schema evolution, and incremental processing for reliable and efficient data pipelines.
• Optimized Spark workloads using partitioning, caching, broadcast joins, predicate pushdown, and optimized transformations to improve performance and reduce processing time.
• Developed parameterized and metadata-driven Databricks notebooks and ETL workflows to enable reusable, scalable, and maintainable data engineering solutions.
• Implemented Medallion Architecture using Azure Databricks, ADLS Gen2, and Delta Lake across Bronze, Silver, and Gold layers for enterprise data processing and analytics.
• Integrated Azure Databricks with Azure Data Factory for orchestration, scheduling, dependency management, monitoring, and end-to-end ETL automation.
• Implemented data quality validations, error handling, logging, and audit mechanisms within PySpark and Databricks pipelines to improve data reliability and operational support.

GMR & EU7 Forecasting Platform - Philips

(2020-05 - 2021-05)

• Developed ETL pipelines using Azure Data Factory and Azure Databricks.
• Worked with source and downstream teams to troubleshoot data issues, validate pipeline outputs, and support production deployments.
• Implemented pipeline monitoring, failure handling, and alerting to improve production support and operational visibility.
• Designed Synapse Analytics warehouse structures and ADLS Gen2 data lake components to support forecasting and analytical workloads.
• Implemented Azure Databricks notebooks for scalable data cleansing, transformation, and business-rule processing.
• Developed and maintained Azure Data Factory pipelines for scheduled ingestion, transformation, and downstream data delivery.
• Designed Synapse Analytics warehouse and ADLS Gen2 data lake. Implemented monitoring and alerting.

Enterprise Data Lake Modernization - Ecolab

(2019-01 - 2019-12)

• Developed Spark, SQL ingestion and transformation pipelines.
• Troubleshot data processing issues, optimized transformation workflows, and supported production data operations.
• Supported data lake governance practices, including standardized processing, data validation, and operational controls.
• Applied data quality checks and validation controls to improve data accuracy and reliability across ingestion pipelines.
• Implemented reusable data transformation logic to support scalable processing across multiple datasets and business requirements.
• Developed Spark and SQL-based ingestion and transformation workflows for enterprise data lake processing.
• Implemented governance and data quality controls.

Customer Reporting & Analytics Platform - TCS

(2018-06 - 2019-01)

• Enhanced reconciliation and reporting processes.
• Supported reporting enhancements, production issue resolution, and deployment activities across the analytics environment.
• Performed data validation and reconciliation across reporting datasets to identify and resolve data discrepancies.
• Developed and optimized SQL queries, stored procedures, and transformation logic for reporting workloads.
• Developed reporting data marts and SQL-based ETL solutions to support business reporting and analytics requirements.
• Enhanced data reconciliation processes to improve consistency between source data and reporting outputs.
• Built reporting data marts and SQL/ETL solutions.

Enterprise Data Warehouse & ETL Modernization - Accenture

(2015-04 - 2018-06)

• Developed enterprise ETL solutions and SQL procedures.
• Supported operational monitoring and production maintenance for enterprise data warehouse and ETL processes.
• Worked with application and business teams to analyze data issues, implement fixes, and validate production data loads.
• Performed SQL and ETL performance tuning to improve processing efficiency and reduce data load bottlenecks.
• Supported production deployments, release management, and troubleshooting of ETL and database-related issues.
• Implemented source-to-target data mappings, transformation rules, and validation logic for enterprise data integration.