Description

Key Responsibilities:

• Build and maintain ETL pipelines using Python and PySpark on AWS Glue and other compute platforms
• Orchestrate workflows with AWS Step Functions and serverless components (Lambda)
• Implement messaging and event-driven patterns using AWS SNS and SQS
• Design and optimize data storage and querying in Amazon Redshift
• Write performant SQL for data transformations, validation, and reporting
• Ensure data quality, monitoring, error handling and operational support for pipelines
• Collaborate with data consumers, engineers, and stakeholders to translate requirements into solutions
• Contribute to CI/CD, infrastructure-as-code, and documentation for reproducible deployments

Required skills and experience:

• Strong experience with Python and Pyspark for large-scale data processing
• Proven hands-on experience with AWS services: Lambda, SNS, SQS, Glue, Redshift, Step Functions
• Solid SQLSQL skills and familiarity with data modeling and query optimization
• Experience with ETL best practices, data quality checks, and monitoring/alerting
• Familiarity with version control (Git) and basic DevOps/CI-CD workflows

Key Skills

Education

Not specified