Praveen Sk

• Developed and optimized advanced SQL queries on Azure Synapse Analytics and Azure data platforms to process and analyze 2M+
transactional records for enterprise reporting.
• Built scalable PySpark transformation workflows in Azure Databricks for data cleansing, enrichment, validation and large-scale data
processing.
• Enhanced Azure Data Factory (ADF) and Databricks ETL pipelines, reducing data processing runtime by 40% through performance
optimization.
• Automated data extraction, reconciliation, validation, and anomaly detection processes using Python, PySpark and SQL, improving data quality
and operational efficiency.
• Performed source-to-target validation, data profiling, and root cause analysis to ensure accuracy and consistency across enterprise data pipelines.
• Performed source-to-target reconciliation, data validation, and root cause analysis for enterprise reporting systems.

• Developed complex SQL transformations using joins, CTEs, window functions, and aggregations to support large-scale data integration and
reporting requirements.
• Designed and maintained PySpark data processing workflows in Azure Databricks for data transformation, cleansing, and validation.
• Built and monitored ETL pipelines using Azure Data Factory (ADF), enabling reliable ingestion and processing of enterprise datasets.
• Automated data quality checks, reconciliation, and validation processes using Python, Pandas, and SQL, reducing manual effort and improving
accuracy.
• Resolved 50+ production data issues through ETL troubleshooting, SQL performance tuning, and root cause analysis, ensuring pipeline stability
and data integrity.