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sai shashank

@saishashank2

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Azure Data Engineer at Hexaware Technologies who built reusable migration pipelines for Oracle-to-Azure data platforms.

India

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At Hexaware Technologies, I contributed to migrating enterprise Oracle datasets to Azure for Ernst & Young (EY). I built reusable, parameter-driven Azure Data Factory pipelines and Databricks orchestration patterns, and developed PySpark transformations with Delta Lake incremental processing.

At Wells Fargo, I designed Azure pipelines for enterprise data processing and analytics, and supported CI/CD and releases using Azure DevOps and ARM Templates. Earlier, at Tech Mahindra, I provided L2/L3 production support for ETL and BI platforms.

Experience

Work history, roles, and key accomplishments

Current

Azure Data Engineer

Current

Hexaware Technologies

Jan 2024 - Present (2 years 10 months)

Contributed to end-to-end migration of enterprise Oracle datasets to Azure, developing ingestion and transformation workflows using Azure Data Factory and Azure Databricks. Built reusable, parameter-driven pipelines and implemented Delta Lake based incremental processing.

Azure Data Factory Azure Databricks PySpark Spark SQL ADLS Gen2 Delta Lake Oracle SQL Data Migration ETL Data Validation Reconciliations

Cloud / Azure Data Engineer

Wells Fargo

Jan 2019 - Dec 2024 (5 years 11 months)

Designed and implemented Azure data pipelines for ingestion, transformation, and movement of structured and semi-structured data from on-premises systems to Azure cloud platforms. Developed end-to-end ETL/ELT workflows using Azure Data Factory, Azure Databricks, ADLS, Azure Synapse Analytics, and Azure SQL.

Azure Data Factory Azure Databricks PySpark Spark SQL ADLS Blob Storage Polybase Azure SQL Azure Synapse Azure SQL DW Azure DevOps ARM Templates SSIS ETL ELT Data Pipelines Data Modeling REST APIs CI CD

ELT Production Support Analyst

Tech Mahindra

Jan 2015 - Dec 2019 (4 years 11 months)

Provided L2/L3 production support for enterprise ETL and BI platforms, ensuring operational stability and timely resolution of incidents, service requests, and scheduled-job failures. Monitored ETL workflows, analyzed job failures, and coordinated corrective actions.

ETL Production Support Incident Management SLA Management Troubleshooting Monitoring Knowledge Management