Description

Key Responsibilities:
• Design, develop, and maintain robust ETL workflows to extract, transform, and load data from various sources to AWS Redshift and other cloud-based systems.
• Leverage AWS Lambda to build serverless computing solutions for real-time data processing and automation.
• Work with AWS data storage and processing services, including S3, Redshift, Athena, and Glue, to support data processing pipelines.
• Utilize Python and PySpark to create and optimize data transformation logic and analytical queries.
• Collaborate with cross-functional teams to understand data requirements and provide efficient data solutions.
• Monitor and optimize cloud-based infrastructure for performance, reliability, and cost efficiency.
• Troubleshoot and resolve data pipeline issues, ensuring minimal downtime and high data accuracy.
• Implement automation and monitoring tools to enhance reliability.

Required Skills and Qualifications:
• Proven experience working with AWS services, including Lambda, Redshift, S3, Athena, Glue, and CloudWatch.
• Strong knowledge and hands-on experience with Python and PySpark for data transformation and processing.
• Expertise in building and optimizing ETL pipelines in a cloud environment.
• In-depth understanding of relational databases, data warehousing, and performance tuning in Redshift.
• Strong problem-solving and troubleshooting skills.
• Familiarity with infrastructure as code tools like AWS CloudFormation.
• Experience with version control (e.g., Git) and CI/CD pipelines.
• Solid understanding of data security, data privacy, and compliance frameworks.
• Ability to work in an agile, fast-paced environment

Key Skills

Education

Bachelor's degree