Data Engineer/Data Analyst/ Business Analyst with experience in data pipelines, ETL processes, SQL, Python, PySpark, AWS, Databricks, and data visualization tools such as Power BI and Tableau. Experience includes data en…
Data Engineer/Data Analyst/ Business Analyst with experience in data pipelines, ETL processes, SQL, Python, PySpark, AWS, Databricks, and data visualization tools such as Power BI and Tableau. Experience includes data engineering roles at Copart and Tata Consultancy Services, along with data analysis internship experience at Smart Bridge in collaboration with IBM.
Implemented schema changes to accommodate evolving business requirements, ensuring scalability for future data growth.
Designed and developed data pipelines to ingest and integrate data from diverse sources, both On-Premises and Cloud-based, using PySpark and AWS services like Neptune, RDS, Redshift, or Aurora.
Developed end-to-end ETL processes using Databricks Live Tables to extract, transform, and load data from diverse sources.
Crafted intricate SQL queries to extract granular insights from large datasets, optimizing data retrieval and analysis.
Developed Python (Spark) scripts for data preprocessing, enhancing data quality and reliability.
Revamped existing data models, introducing efficient fact and dimension tables, leading to a 20% improvement in query performance.
Increased overall data processing efficiency by 25% through optimization of ETL workflows and data models.
Contributed to a travel website project for streamlined data retrieval, optimization resulting in a 10% project efficiency gain.
Implemented PostgreSQL, AWS Redshift for streamlined data retrieval, applying indexing and query optimization techniques.
Utilized Airflow to orchestrate and automate complex workflows, ensuring seamless execution of ETL tasks.
Employed window functions and Common Table Expressions (CTEs) to simplify and optimize complex data retrieval scenarios.
Leveraged AWS Glue for efficient data cleansing, filtering, and aggregation, ensuring data quality and consistency.
Led the "Bike Buyer" project, utilizing clustering techniques to achieve a 12% improvement in prediction accuracy.
Applied data clustering methodologies to group customers, enhancing targeted marketing and prediction precision.
Played a key role in data acquisition, utilizing advanced SQL queries to extract, clean, and transform data from diverse sources.
Managing workflows with Jira and Rally, implementing continuous integration with Jenkins for data-driven projects.
Received the 'Team of Excellence' award for outstanding leadership and contribution to the project's success.
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