Arihant Jain

Designed ETL/ELT Azure lakehouse pipelines using Azure Data Factory, ADLS Gen2, Azure Databricks, Delta Lake, Unity Catalog, PySpark, SQL, and Power BI for ingestion, transformation, data governance, and reporting.
Migrated data processing from Azure Data Factory to Azure Databricks with Unity Catalog, consolidating extraction, transformation, and loading into a data governance-enabled platform that reduced costs and improved performance.
Optimized Databricks and PySpark workloads using parallel processing, reusable transformation logic, and on-demand execution, reducing file processing time by 30% and pipeline creation/execution time by 35%.
Implemented data quality, validation, and reconciliation checks across curated layers to improve downstream reporting reliability and reduce production issues.
Built production failure alerting for critical pipelines using Azure Logic Apps, improving monitoring visibility and reducing manual monitoring effort by 30%.
Automated Power BI dataset refreshes using Azure Data Factory and Power BI API calls, reducing manual dependency on the Power BI team by 20%.
Led 3 Data Engineers and 1 Power BI Developer by reviewing approaches, coordinating development, resolving blockers, and minimizing rework.

Designed ETL/ELT Azure lakehouse pipelines using Azure Data Factory, ADLS Gen2, Azure Databricks, Delta Lake, Unity Catalog, PySpark, SQL, and Power BI for ingestion, transformation, data governance, and reporting., Migrated data processing from Azure Data Factory to Azure Databricks with Unity Catalog, consolidating extraction, transformation, and loading into a data governance-enabled platform that reduced costs and improved performance., Optimized Databricks and PySpark workloads using parallel processing, reusable transformation logic, and on-demand execution, reducing file processing time by 30% and pipeline creation/execution time by 35%., Implemented data quality, validation, and reconciliation checks across curated layers to improve downstream reporting reliability and reduce production issues., Built production failure alerting for critical pipelines using Azure Logic Apps, improving monitoring visibility and reducing manual monitoring effort by 30%., Automated Power BI dataset refreshes using Azure Data Factory and Power BI API calls, reducing manual dependency on the Power BI team by 20%., Led 3 Data Engineers and 1 Power BI Developer by reviewing approaches, coordinating development, resolving blockers, and minimizing rework.

Developed Bronze, Silver, and Gold pipelines in Azure Databricks using PySpark, Spark SQL, and Delta Lake for cleansing, transformation, data governance, enrichment, and curated modeling.
Contributed to the full SDLC, including requirement gathering, design, development, testing, deployment, documentation, and business evaluation using Agile methodologies.
Created stored procedures and functions for auditability, capturing record-level details in Azure Databricks and pipeline-level details in Azure Data Factory for data governance traceability.
Worked with product owners, business users, QA, and reporting teams to convert requirements into scalable Azure data engineering solutions.
Contributed to data modeling and data warehousing, including star schema design and SCD implementation patterns for analytical reporting.
Resolved business incidents with an average turnaround time of less than a day and supported QA and production deployments.

Developed Bronze, Silver, and Gold pipelines in Azure Databricks using PySpark, Spark SQL, and Delta Lake for cleansing, transformation, data governance, enrichment, and curated modeling., Contributed to the full SDLC, including requirement gathering, design, development, testing, deployment, documentation, and business evaluation using Agile methodologies., Created stored procedures and functions for auditability, capturing record-level details in Azure Databricks and pipeline-level details in Azure Data Factory for data governance traceability., Worked with product owners, business users, QA, and reporting teams to convert requirements into scalable Azure data engineering solutions., Contributed to data modeling and data warehousing, including star schema design and SCD implementation patterns for analytical reporting., Resolved business incidents with an average turnaround time of less than a day and supported QA and production deployments.

Led technical data mapping across TPSS, SFDC, and MSD source systems for governed data integrity and Salesforce alignment.
Developed an enterprise-scale ETL pipeline using PySpark to migrate 2M+ customer records into Salesforce using AWS Glue, Amazon S3, AWS AppFlow, and Apache Airflow.
Implemented a PySpark data quality and data governance framework for validation of critical fields, improving migration accuracy and governance compliance for emails and phone numbers.
Reduced data ingestion time by 60% and deployment effort by 50% using PySpark distributed processing and modular, reusable pipeline templates.
Automated secure ingestion of 30,000+ customer attachments into Salesforce, eliminating manual processing and preserving record retention.

Led technical data mapping across TPSS, SFDC, and MSD source systems for governed data integrity and Salesforce alignment., Developed an enterprise-scale ETL pipeline using PySpark to migrate 2M+ customer records into Salesforce using AWS Glue, Amazon S3, AWS AppFlow, and Apache Airflow., Implemented a PySpark data quality and data governance framework for validation of critical fields, improving migration accuracy and governance compliance for emails and phone numbers., Reduced data ingestion time by 60% and deployment effort by 50% using PySpark distributed processing and modular, reusable pipeline templates., Automated secure ingestion of 30,000+ customer attachments into Salesforce, eliminating manual processing and preserving record retention.