Description

You will build and maintain ETL pipelines using Python, PySpark, and AWS Glue to handle large-scale data processing.

This role is on-site.

Responsibilities

• Orchestrate serverless workflows using AWS Step Functions and Lambda.
• Implement event-driven messaging patterns with AWS SNS and SQS.
• Design, optimize, and query data storage in Amazon Redshift using performant SQL.
• Ensure data quality, monitoring, error handling, and operational support for pipelines.
• Contribute to CI/CD, infrastructure-as-code, and documentation for reproducible deployments.

Required Skills

• 5+ years of strong experience with Python and PySpark for data processing.
• Hands-on experience with AWS services: Lambda, SNS, SQS, Glue, Redshift, and Step Functions.
• Solid SQL skills with familiarity in 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.
• Ability to translate requirements into solutions by collaborating with data consumers and engineers.

Preferred Skills

• Experience with additional AWS compute platforms beyond Glue.
• Background in operational support for production data pipelines.

Key Skills

Education

Any Gradute