Ashwani Singh

Developed and enhanced ETL workflows using Azure Data Factory and Azure Databricks.
Implemented distributed data system performance tuning, improving query efficiency by 40%.
Designed and maintained scalable ETL pipelines using PySpark and Airflow, processing 50M records daily.
Designed an automated Data Comparison framework in Databricks to validate and reconcile source and target datasets.
Led the development of invoice validation framework from scratch overseeing its entire lifecycle.
Automated issue logging by enabling direct ticket creation in Devops eliminating manual Excel tracking.

Developed and enhanced ETL workflows using Azure Data Factory and Azure Databricks., Implemented distributed data system performance tuning, improving query efficiency by 40%., Designed and maintained scalable ETL pipelines using PySpark and Airflow, processing 50M records daily., Designed an automated Data Comparison framework in Databricks to validate and reconcile source and target datasets., Led the development of invoice validation framework from scratch overseeing its entire lifecycle., Automated issue logging by enabling direct ticket creation in Devops eliminating manual Excel tracking.

Automated data validation and cleansing using PySpark, increasing pipeline reliability and accuracy.
Configured and monitored job scheduling using Airflow for timely execution.
Collaborated with analysts and stakeholders to provide tailored ETL solutions.
Translated raw data into informative reports and visuals to support data-driven decisions for management and stakeholders.

Automated data validation and cleansing using PySpark, increasing pipeline reliability and accuracy., Configured and monitored job scheduling using Airflow for timely execution., Collaborated with analysts and stakeholders to provide tailored ETL solutions., Translated raw data into informative reports and visuals to support data-driven decisions for management and stakeholders.