Pipeline Development: Design, develop, and maintain end-to-end ETL/ELT pipelines using Python and PySpark.
Big Data Processing: Build large-scale data processing frameworks to handle structured and unstructured data, ensuring high performance and reliability.
Cloud Infrastructure: Architect and manage data solutions within the GCP ecosystem, focusing on cost-efficiency and security.
Data Modeling: Design and implement robust data warehouse models (Star/Snowflake schemas) and data lake architectures.
Optimization: Identify, design, and implement internal process improvements, such as automating manual processes and optimizing data delivery for greater scalability.
Collaboration: Work closely with stakeholders to understand data requirements and translate them into technical specifications.
