I worked as a Data Engineer Intern, where I developed and optimized ETL pipelines using Python and Apache Airflow, improving data processing efficiency by 30%. I designed and implemented data models in Snowflake to support analytics across multiple product teams, enhancing data accessibility by 25%. I also collaborated with data scientists to integrate machine learning models into data pipelines, boosting predictive analytics capabilities and enabling data-driven decision-making within the organization.

I worked as an Azure Data Engineer, where I designed and developed batch and real-time data processing solutions using Azure Data Factory, Databricks, and Azure Stream Analytics. I was responsible for optimizing data pipelines with Apache Spark and PySpark, leading to significant improvements in performance and cost efficiency. I also implemented real-time streaming solutions with Kafka, managed NoSQL databases like MongoDB and Cassandra, and developed data warehousing solutions using Snowflake and Azure Synapse to enhance data accessibility and streamline processing workflows.

I worked as a Junior Data Engineer, where I developed and maintained data quality checks using Python and SQL, significantly reducing data inconsistencies. I optimized complex SQL queries for ad-hoc reporting, improving query execution time and enabling faster decision-making. I also collaborated with cross-functional teams to design and implement a metadata management system, enhancing data discoverability and reducing time spent on data-related inquiries. This role helped improve data reliability, performance, and overall data management processes.