Ahraaz Mohd

At The Marketing Store, I supported the modernization of legacy data infrastructure by implementing scalable, cloud-native solutions using Microsoft Fabric. This initiative focused on migrating on-premises and traditional data warehouses to the Fabric platform, improving data accessibility, performance, and scalability.

I built end-to-end data pipelines using the Data Factory experience within Microsoft Fabric and Databricks to orchestrate the ingestion and transformation of enterprise data. Within Fabric, I configured data lineage and cataloging to enhance governance and simplify discovery. I also developed real-time analytics pipelines and Power BI dashboards using Direct Lake mode and semantic models, enabling timely, actionable business insights. These efforts reduced data latency, improved reporting, and ensured seamless integration across the Azure ecosystem.

This project focused on integrating and optimizing marketing campaign performance analytics across diverse data sources. I developed a hybrid solution using Azure Synapse for real-time queries and Snowflake for scalable storage and transformation of structured and semi-structured data. I built robust ETL pipelines with Azure Databricks and HDInsight, supporting large-scale ingestion from Azure Data Lake. Data models were curated in Power BI with DAX for interactive executive dashboards. By enhancing pipeline efficiency and introducing data quality checks, I reduced data delivery timelines by 40% and improved the accuracy of campaign ROI analysis. This project demonstrated my ability to harmonize modern data platforms for a tangible business impact.

At RBC, I contributed to the modernization of their enterprise data warehouse by implementing scalable and efficient data solutions in Microsoft Azure. I developed ETL pipelines using Azure Data Factory and Databricks to ingest and transform data from various source systems into Azure Data Lake and Azure Synapse Analytics.

My responsibilities included configuring Databricks jobs, refactoring notebooks for performance optimization, and implementing PySpark-based ingestion frameworks. I also tuned Spark and Hive jobs to ensure efficient data processing.

This project demonstrates my ability to build and optimize modern cloud-based data solutions that drive real-time analytics and informed decision-making.