• Around 3 years working with major relational and NoSQL databases, proficient in data structure design and optimization.• Skilled in building efficient ETL pipelines with AWS Glue, Dataiku, and Spark, and improving code maintainability with Kedro.• Demonstrated ability to deploy and manage data solutions on AWS, Azure ensuring high availability, scalability, and security.• Adept in orchestrating data workflows with Airflow, successfully automating processes for increased efficiency.• Collaborative and skilled at translating complex technical concepts into clear insights for smooth stakeholder collaboration.• Proficient in using Git and Bitbucket for code version control, collaboration, and maintaining clean development history.

• Skilled in wrangling data across various environments, including importing and exporting data between relational databases (like MySQL) and big data tools like HDFS, Hive, and HBase using Sqoop.• Adept at analyzing massive datasets through writing optimized Hive queries, leveraging its capabilities for efficient data querying and manipulation.• Possess a strong understanding of Spark architecture and its in-memory processing capabilities, allowing for fast and efficient data manipulation and analysis on large datasets.

• Learned PySpark for big data manipulation, wielded Hive queries for insightful mining, and grasped the power of distributed computing through Hadoop and MapReduce.• Transformed raw clickstream data with PySpark into gold – actionable insights that boosted website recommendations. Hive queries weren't just code, they were customer behavior revelations guiding marketing campaigns.