With around 9 years of experience as a Data Engineer, I have consistently demonstrated my expertise in designing, developing, and implementing data models for enterprise-level applications and BI solutions. My extensive experience with Azure cloud technologies, including Azure Data Lake Storage, Azure Data Factory, Azure SQL, and Azure Synapse Analytics, coupled with my solid knowledge of AWS services such as AWS EMR, AWS Redshift, AWS S3, and AWS EC2, has enabled me to deliver scalable and efficient data solutions. My proficiency in configuring servers for auto-scaling and elastic load balancing further underscores my ability to manage cloud environments effectively.I am skilled in designing reusable Informatica workflows and leveraging Python for data manipulation and analysis using libraries like NumPy, Pandas, and SciPy. My experience extends to NoSQL databases such as HBase, Cassandra, and MongoDB, as well as SQL databases like Teradata, Oracle, and SQL Server. I have a strong background in working with Hadoop technologies, utilizing components for large-scale data processing, and analyzing data with tools like MapReduce, Hive, and Pig. Additionally, I have developed and optimized Spark applications for real-time data processing and analytics, integrated Spark with Kafka for stream processing, and tuned Spark jobs for optimal performance.I have hands-on experience with Snowflake’s security features, ensuring data privacy and compliance, and I am proficient in using Power BI and Tableau for data visualization and reporting. My skills in optimizing data visualization tools, performing data transformations, and implementing Git workflows have enabled me to deliver high-quality, reliable data solutions. Overall, my technical expertise and experience across a wide range of tools and technologies make me a valuable asset for any data engineering role.