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