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We are seeking an experienced Data Engineer with expertise in PySpark, Snowflake and AWS to join our team. The ideal candidate will design, develop, and optimize data pipelines and workflows in a cloud-based environment, leveraging AWS services, PySpark, and Snowflake for data processing and analytics
Job Responsibility:
Design and implement scalable ETL pipelines using PySpark on AWS
Develop and optimize data workflows for Snowflake integration
Manage and configure AWS services such as S3, Lambda, Glue, EMR, and Redshift
Collaborate with data analysts and business teams to understand requirements and deliver solutions
Ensure data security and compliance with best practices in AWS and Snowflake environments
Monitor and troubleshoot data pipelines and workflows for performance and reliability
Write efficient, reusable, and maintainable code for data processing and transformation
Requirements:
Strong experience with AWS services (S3, Lambda, Glue, MSK, etc.)
Proficiency in PySpark for large-scale data processing
Hands-on experience with Snowflake for data warehousing and analytics
Solid understanding of SQL and database optimization techniques
Knowledge of data lake and data warehouse architectures
Familiarity with CI/CD pipelines and version control systems (e.g., Git)
Strong problem-solving and debugging skills
Experience with Terraform or CloudFormation for infrastructure as code
Knowledge of Python for scripting and automation
Familiarity with Apache Airflow for workflow orchestration
Understanding of data governance and security best practices
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