About me

I am a data professional with a strong educational background and over seven years of experience in the field. In May 2023, I graduated from the University of Connecticut, where I honed my skills in data engineering, analytics, and machine learning through a combination of rigorous coursework and hands-on projects. Throughout my career, I have excelled in leveraging cloud technologies to streamline ETL processes and optimize data processing for seamless deployment, consistently delivering production-ready projects on time. My expertise extends to database schema modeling and design, encompassing both relational databases such as Oracle, SQL Server, and PostgreSQL, as well as NoSQL databases like MongoDB and Cassandra. I am proficient in implementing ETL processes using tools like AWS services, SSIS and Apache Airflow and Snowflake services. Python is my go-to language for backend configurations, data manipulations/transformations and workflow automation, supported by libraries such as Pandas, NumPy, Matplotlib, TensorFlow, and scikit-learn, as well as Plotly for data visualization. I have successfully managed projects using industry-standard tools like Jira, Terraform, GitLab, and Confluence, fostering seamless collaboration. My skills also extend to data streaming using PySpark, Kafka, and Apache Airflow, as well as a demonstrated proficiency in big data technologies like Hadoop, Hive, and Sqoop. I take pride in my ability in managing my team and mentor them, promote continuous learning, and uphold high standards in data engineering practices. Additionally, I excel in data migration, cleansing, and profiling, ensuring data accuracy and quality throughout the data warehousing process. While my primary expertise lies in AWS, I have also briefly worked with Azure services such as Azure VMs, Blob Storage, Azure Functions, Azure Synapse, Azure Data Factory, and Data Lakes, adding versatility to my skill set.