As a data Engineer for an investment-management firm client, I architect and optimize large-scale web scraping and ETL solutions to enhance data-driven decision-making. Leveraging advanced technologies, I focus on efficient data extraction, processing, and analysis.Key aspects of my role include:- Designing and implementing robust ETL pipelines using Scrapy, S3, Snowflake, and Apache Airflow for seamless data extraction and transformation.- Utilizing AWS (S3, EC2, CloudWatch, Lambda) for scalable cloud infrastructure and managing data storage with Snowflake and PostgreSQL for high performance.- Implementing monitoring and alerting systems with PagerDuty for quick issue resolution and minimal downtime.- Coordinating with cross-functional teams using Jira for agile project management and continuous improvement.- Leveraging additional tools like GitHub, Jenkins, Playwright, and Selenium for efficient development, testing, and deployment processes.

As a seasoned Data Engineer, I excel in architecting and optimizing robust data pipelines to facilitate informed decision-making in fast-paced environments. My expertise spans:- Designing and implementing scalable ETL pipelines using tools like SQLAlchemy, ensuring efficient data extraction, transformation, and loading.- Managing data integrity and validation processes across MySQL, Microsoft SQL Server, and other SQL-based environments.- Leveraging AWS services (S3, EC2, Lambda) for cloud-based solutions, enhancing data processing and management capabilities.- Implementing effective web scraping techniques and integrating REST APIs to enrich data pipelines and support analytical workflows.- Driving data governance initiatives, including metadata management, data lineage tracking, and ensuring compliance with industry standards.My proficiency in Python enables me to build sophisticated data models and streamline data ingestion processes, empowering organizations to harness their data for strategic insights and operational excellence.