Description

You will design, build, and maintain scalable ETL and data processing pipelines using Python, PySpark, and AWS Glue.

This role is on-site.

Responsibilities

• Develop cloud-native data solutions leveraging AWS services including S3, Lambda, Step Functions, ECS, SNS, and SQS.
• Optimize Spark jobs, SQL queries, and data workflows for performance and scalability.
• Implement software engineering best practices including version control, automated testing, CI/CD, and code reviews.
• Monitor production data environments, troubleshoot issues, and perform root cause analysis.
• Collaborate with cross-functional teams to deliver technical documentation and support operational excellence.

Required Skills

• 5+ years of hands-on experience building production-grade data pipelines using Python and PySpark.
• Expertise with AWS cloud services: S3, Glue, Lambda, Step Functions, ECS, SNS, and SQS.
• Strong SQL and PL/SQL development experience with relational databases such as Oracle or PostgreSQL.
• Experience with Spark performance tuning and large-scale data processing.
• Solid understanding of software development best practices including Git, testing, and CI/CD pipelines.
• Experience with monitoring, observability, alerting, and production support processes.
• Bachelor's degree in Computer Science or related field.

Preferred Skills

• Experience working with enterprise-scale cloud data platforms and familiarity with DevOps practices.
• Knowledge of data governance, security, and compliance standards.

Key Skills

Education

Bachelor's degree