Mohan k

Architected and led the development of end-to-end ETL/ELT pipelines following the Medallion Architecture (Bronze, Silver, Gold) in Azure Databricks. Built ingestion workflows in Azure Data Factory integrating on-premise MySQL, Oracle, and various flat-file feeds into Azure Blob Storage. Transformed and curated large-scale datasets using PySpark on Azure Databricks, implementing partitioning and op

Architected and implemented data ingestion pipelines from on-premise Oracle and PostgreSQL databases to AWS S3, ensuring continuous and scalable data movement. Developed a robust Python-based jet parser on EC2 (Red Hat Linux) for 24/7 ingestion of XML and JSON files, leveraging auto-scaling and bash scripting for reliability. Built ETL processes using AWS Glue and PySpark to load and transform dat

Developed PySpark applications for JSON to DataFrame conversions and element-level comparisons, optimizing healthcare data transformation. Automated data ingestion and transformations using Azure Data Factory, ensuring reliable scheduling and orchestration of large-scale healthcare datasets. Executed end-to-end migrations from Oracle to Azure SQL Database and Cosmos DB, including data type convers

Migrated on-premise Oracle data to Cloud Storage using Python scripts and Storage Transfer Service, ensuring schema conversions and data consistency. Designed and orchestrated scalable ETL workflows in Dataflow for streaming and batch processing of large marketing datasets. Developed PySpark jobs on Dataproc to transform and analyze data stored in Cloud Storage, implementing best practices for job

Designed and architected scalable data processing pipelines on AWS (EMR, Redshift, Athena, Glue) based on business requirements. Ingested data from diverse sources (HDFS, RDBMS) using Sqoop and custom ETL scripts, loading into S3 and Redshift. Developed Spark/PySpark workflows on EMR for data cleansing, enrichment, and transformation, ensuring high performance and low latency.

Analyzed business requirements for telematics data ingestion and real-time analytics, collaborating with stakeholders to shape a robust solution. Architected ingestion pipelines using Pub/Sub and Dataflow for streaming telematics data into BigQuery. Executed large-scale batch jobs on Dataproc (Hive, Spark, Sqoop) to transform data from on-premise systems into GCP.

Implemented ETL workflows using AWS Glue to move data from HDFS to S3, ensuring data quality and security throughout. Migrated Hive-based queries to EMR and Spark for scalable data transformations, reducing processing times significantly. Built Lambda functions and Step Functions for event-driven data pipelines, enabling automated, near real-time risk alerts.

Designed and developed Web Intelligence reports, including sub-reports, hierarchies, filters, and prompts for advanced user interactions. Built User-Defined Objects and Report Variables (e.g., @AggregateAware) to generate summary-level and drill-down views. Optimized BO reports for performance, implementing best practices in universe design and database connectivity.

Completed a Bachelor of Technology in Electronics & Communication Engineering. This program provided a strong foundation in engineering principles and problem-solving.