Mohan A

• Designed and optimized high-performance ETL/ELT pipelines using Talend, Informatica, Matillion, Python, and Apache Spark on AWS EMR, processing 500M+ daily banking transactions while ensuring SOX, PCI-DSS, and GLBA compliance. • Built real-time streaming data pipelines using Apache Kafka and AWS Kinesis integrated with Spark Streaming, enabling sub-second processing of card authorization events for fraud detection and financial analytics. • Architected enterprise data warehouse solutions on Snowflake and AWS Redshift, implementing star schemas, SCD Type 2 models, and optimized SQL transformations to support regulatory reporting, risk analytics, and Tableau dashboards. • Led cloud modernization and DevOps initiatives, migrating legacy systems to AWS S3 data lake, Redshift, and Snowflake, while implementing Airflow orchestration, CI/CD pipelines, Docker/Kubernetes deployments, and enterprise data governance using Collibra.

• Designed and implemented scalable ETL/ELT pipelines using Talend, Informatica, Matillion, and Apache Spark to process 500M+ daily retail transactions from POS and e-commerce systems into Azure Synapse Analytics and Snowflake, ensuring PCI-DSS compliant data governance and secure access controls. • Built real-time and batch data processing architectures using Kafka, Spark Streaming, Azure Event Hubs, and Databricks, enabling sub-second streaming analytics for clickstream, payment, and fraud detection use cases. • Architected cloud lakehouse and data warehousing solutions on Azure and Snowflake, designing optimized data models, SCD pipelines, and performance-tuned SQL transformations to support enterprise analytics, Customer 360 views, and Power BI reporting. • Led cloud modernization and DevOps initiatives, migrating legacy warehouses to Azure Synapse and Snowflake, implementing CI/CD pipelines, Airflow orchestration, Docker/Kubernetes deployments, and enterprise data governance frameworks (Alation, Apache Atlas).

• Designed and built scalable ETL/ELT pipelines on GCP using Dataflow, Apache Beam, Talend, and Informatica, integrating healthcare data from HL7 feeds, APIs, Cloud SQL, and Firestore while ensuring HIPAA-compliant data processing and interoperability. • Developed real-time and batch data pipelines using Kafka, Cloud Pub/Sub, Apache Spark, and Flink to ingest and process 2M+ daily patient records from EHR systems, securely loading PHI data into BigQuery and Snowflake for enterprise analytics. • Implemented a cloud lakehouse architecture on GCP (Cloud Storage, BigQuery, Dataflow) and optimized data models, partitioning strategies, and SQL transformations, enabling scalable clinical analytics and reporting. • Built containerized data pipeline infrastructure using Docker, Kubernetes (GKE), and Airflow, implementing CI/CD pipelines with GitLab and data quality validation using Great Expectations, ensuring reliable, secure, and compliant healthcare data workflows.

• Developed scalable ETL pipelines using Python, SQL, and AWS Glue to ingest and transform data from multiple sources into centralized data warehouses and data lakes. • Designed AWS S3 data lakes and built optimized data models in Snowflake and Amazon Redshift to support high-performance analytics and reporting. • Automated data ingestion and workflow orchestration using Apache Airflow, AWS Lambda, and Python for reliable batch and real-time data processing. • Integrated data from Oracle, SQL Server, and AWS RDS, ensuring data quality and enabling business insights through Tableau and AWS QuickSight dashboards.

Developed and maintained ETL pipelines to process structured and unstructured data using SQL, Python, and Azure Data Factory. Built data storage solutions in Azure Data Lake and Azure SQL Data Warehouse, implemented data quality checks, and optimized data models for analytics. Integrated data from multiple sources and supported reporting and dashboards using Power BI to enable data-driven decision making.