Ravi Teja

● Designed scalable ETL pipelines using ADF, Databricks and Snowflake. ● Developed PySpark transformations and implemented data quality checks. ● Automated multi‑source ingestion and Medallion architecture layers. ● Integrated Snowflake with Databricks and automated validation workflows. ● Developed and implemented time series forecasting ML models (XG Boost, DLT, Theta, STL ARIMA, Holt-Winters, STL ETS) to analyze trends, improve prediction accuracy and business forecasting. ● Developed and optimized PySpark jobs in Databricks to process and transform large-scale datasets. ● Designed, built, and maintained end-to-end Azure Data Factory pipelines for data ingestion, transformation, and orchestration. ● Integrated Snowflake with Azure Databricks to enable efficient data storage, processing, and analytics workflows. ● Automated Snowflake data operations (loads, tasks, and processes) to streamline data pipelines and reduce manual intervention. ● Migrated legacy Python data-processing code to scalable PySpark implementations in Databricks to improve performance and reliability. ● Implemented data quality checks and validation rules to meet client requirements and ensure accuracy of downstream reports. ● Developed and managed CI/CD pipelines for Databricks notebooks and SQL scripts to automate testing, deployment, and version control across development environments. ● Implemented email-based notifications and alerting for Azure Data Factory pipeline status and failures. ● Configured and integrated Control-M with ADF to schedule, orchestrate, and deploy data workflows. ● Managed end-to-end pipeline configurations, deployments, and monitoring through Control-M to ensure reliable execution and SLA compliance. ● Implemented data governance and metadata management frameworks using Azure Purview and Databricks Unity Catalog to ensure data lineage tracking, classification, and compliance with regulatory requirements.

● Built ELT pipelines leveraging Snowflake and UC4 scheduler ● Performed data modeling, profiling and mapping for BI analytics ● Automated data ingestion from AWS S3 via shell scripts ● Conducted comprehensive data profiling and mapping activities to analyze data structure, quality, and relationships across source systems. ● Utilized SVN (Subversion) for version control and automated generation of ETL scripts with custom extensions to streamline development processes. ● Implemented and managed AWS S3 storage solutions for scalable data lake architecture and cloud-based data storage. ● Developed shell scripts for automated data extraction from S3 buckets and performed data cleanup operations to ensure data integrity. ● Configured and utilized UC4 and Snowflake data loading tools for efficient data ingestion and warehouse operations. ● Scheduled and orchestrated ETL job execution in UC4 scheduler, ensuring timely data loading into core database tables. ● Performed code debugging, optimization, and fixes to resolve issues and improve ETL pipeline performance and reliability.

Experienced Data Analytical Engineer with a demonstrated history of working in the Retail services. Skilled in Bigdata with Azure ,Hadoop,Spark ,Hive ,and Management.

● Designed and provisioned Azure Blob Storage containers for scalable cloud data storage and management across multiple environments. ● Implemented comprehensive Azure Data Factory solutions including end-to-end pipeline development, dataset configuration, and dataflow orchestration for ETL/ELT processes. ● Leveraged Azure HDInsight clusters to deploy and manage Hadoop ecosystems, utilizing Hive and other Hadoop-based frameworks for large-scale data processing and ETL operations. ● Developed and optimized Spark SQL queries, Data Frames, and Datasets for efficient data transformation, analysis, and processing in distributed computing environments.