Kanchana singh

• Designed and maintained scalable data pipelines utilizing Azure Data Factory and Databricks to seamlessly integrate data from diverse sources, including on-premises systems, SaaS applications, and APIs into Azure Data Lake.
• Led the migration of legacy ETL workloads to Microsoft Fabric, effectively leveraging Lakehouses and Warehouses to centralize and optimize organizational data.
• Developed high-performance PySpark scripts in Azure Databricks for complex data transformations and cleansing, achieving a significant 30% reduction in processing time.
• Implemented robust Data Governance frameworks to ensure data quality, security, and compliance across all data assets.
• Collaborated with cross-functional teams to identify data needs and deliver actionable insights, enhancing decision-making processes.

• Architected and delivered end-to-end data pipelines on Databricks Lakehouse, ingesting and processing
KYC data for a large-scale financial services client, enabling real-time compliance reporting.
• Built scalable ETL/ELT processes using Apache Spark (PySpark) and Azure Data Factory, reducing data
processing time by 25% through Spark optimization techniques (partitioning, caching, broadcast joins).
• Implemented Delta high-performance data storage, enabling
• Integrated SQL warehousing Enforced governance aligned Catalog control lineage tracking. Collaborated with architects and business to requirements
scalable, data models. Diagnosed and resolved bottlenecks, achieving improvement and Developed and maintained comprehensive data quality validation frameworks and consistency analytics.