Python: Proven expertise in leveraging Python for data manipulation, scripting, and the development of robust data-driven applications.
PySpark: In-depth experience in building and optimizing scalable data processing workflows using PySpark for distributed data transformation.
SQL: Strong command of SQL with the ability to craft complex queries, perform performance tuning, and design efficient relational databases.
AWS: Practical experience in architecting, deploying, and maintaining data solutions on AWS platforms such as S3, EMR, Glue, Lambda, and more.
Data Engineering: Comprehensive understanding of data warehousing architectures, ETL/ELT strategies, and best practices in designing resilient data pipelines.
Professional Skills: Demonstrated strength in analytical thinking, problem-solving, and clear communication. Capable of working independently as well as in a collaborative team setting.
Desired skills :
Airflow: Hands-on experience in orchestrating, scheduling, and monitoring data workflows using Apache Airflow.
Snowflake: Working knowledge of Snowflake's cloud data warehousing features and its use in analytical processing.
Version Control: Proficient in using Git-based version control systems such as Bitbucket for collaborative code management and CI/CD workflows.
