Rahul S

Designed and implemented large-scale AWS data pipelines for retail banking and fraud detection platforms. • Developed PySpark jobs on Amazon EMR to ingest, cleanse, and transform high-volume transaction, account, and payment data. • Orchestrated end-to-end ETL workflows using AWS Step Functions with retries, dependency handling, and SLA enforcement. • Integrated Amazon SQS for decoupled ingestion and Amazon SNS for real-time alerts on pipeline failures and data anomalies. • Optimized Spark workloads and Amazon Redshift queries, reducing overall processing time by up to 40%. • Implemented monitoring, logging, and security controls using AWS CloudWatch and IAM to meet PCI DSS and AML compliance. • Automated CI/CD pipelines using Git and Jenkins to ensure reliable, version-controlled deployments.

Designed and delivered end-to-end data pipelines on Google Cloud Platform (GCP) for healthcare and clinical datasets. • Built PySpark jobs on Google Cloud Dataproc to process patient, claims, and operational data at scale. • Stored raw and curated datasets in Google Cloud Storage (GCS) and BigQuery to support analytics and reporting. • Implemented event-driven ingestion using Google Cloud Pub/Sub to decouple pipeline components. • Collaborated with data analysts, QA teams, and business stakeholders to validate data quality and ensure timely delivery of insights.

Built analytical data pipelines using SQL and PySpark to support banking operations and business reporting. • Processed large customer and operational datasets to identify trends and inefficiencies across products and regions. • Orchestrated scheduled workflows using AWS Step Functions to ensure timely data refreshes. • Integrated Amazon SQS and SNS for event-driven ingestion and alerting on pipeline failures. • Developed Tableau dashboards to visualize KPIs for leadership and operations teams. • Ensured data accuracy, access controls, and audit logging to support regulatory compliance.