As a data professional, I have collaborated with business stakeholders to detect fraudulent transactions through both stream and batch processing, utilizing the latest cloud technologies. My expertise lies in building robust data pipelines capable of processing over 20GB per hour, ensuring efficient and scalable data management solutions.Key Achievements: -> Fraud Detection: Partnered with business stakeholders to develop and implement systems that identify fraudulent transactions, enhancing security and trust in financial processes. -> Scalable Data Pipelines: Engineered data pipelines that efficiently process high volumes of data (20GB/hr) using industry-leading tools and technologies.Proficiency in -> Cloud Technologies: Extensive experience with Azure Databricks, Azure Data Factory, and Azure Synapse for creating and managing large-scale data processing frameworks. -> Big Data Tools: Leveraging Apache Spark and Apache Kafka for stream and batch data processing. -> Monitoring & CI/CD: Implemented monitoring and continuous integration/continuous deployment (CI/CD) practices using Datadog, SonarQube, Bitbucket, Jenkins, and XLR. -> Programming Languages: Skilled in PySpark, Python, SQL, and Spark-SQL