Data Engineer

Role Overview: As a skilled Data Engineer with expertise in Python, Snowflake, and AWS, your main responsibility will involve constructing and improving scalable data pipelines, integrating various data sources, and supporting analytics and business intelligence solutions within a cloud-based environment. A significant aspect of your role will revolve around designing and overseeing AWS Glue Jobs and facilitating efficient, serverless ETL workflows. Key Responsibilities: - Develop and execute robust data pipelines using AWS Glue, Lambda, and Python. - Collaborate extensively with Snowflake for tasks related to data warehousing, modeling, and analytics. - Manage ETL/ELT jobs using AWS Glue to guarantee end-to-end data reliability. - Responsible for data migration between CRM systems, particularly from Snowflake to Salesforce, in adherence to specified business rules to ensure data accuracy. - Optimize SQL/SOQL queries, manage large data volumes, and sustain high levels of performance. - Implement data normalization and quality checks to maintain accurate, consistent, and deduplicated records. Qualifications Required: - Proficient programming capabilities in Python. - Hands-on experience with Snowflake Data Warehouse. - Proficiency in AWS services such as Glue, S3, Lambda, Redshift, and CloudWatch. - Experience with ETL/ELT pipelines and data integration using AWS Glue Jobs. - Proficiency in SQL and SOQL for data extraction and transformation. - Understanding of data modeling, normalization, and performance optimization. - Familiarity with Salesforce Data Loader, ETL mapping, and metadata-driven migration is considered a plus. - Experience with CI/CD tools, DevOps, and version control systems like Git. - Background of working in Agile/Scrum environments is desirable.,