Description

Key Responsibilities

Design, build, and maintain scalable ETL and data processing pipelines using Python, PySpark, and AWS Glue

Develop cloud-native data solutions leveraging AWS services such as S3, Lambda, Step Functions, ECS, SNS, and SQS

Optimize Spark jobs, SQL queries, and data workflows for performance and scalability

Develop and maintain PL/SQL scripts and database solutions in Oracle, PostgreSQL, or similar relational databases

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 including architects, developers, analysts, and business stakeholders

Maintain technical documentation and support operational excellence initiatives

Required Qualifications

Strong hands-on experience building production-grade data pipelines using Python and PySpark

Expertise with AWS cloud services including 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

Strong analytical, troubleshooting, and problem-solving skills

Excellent verbal and written communication skills

Preferred Qualifications

Experience working with enterprise-scale cloud data platforms

Familiarity with DevOps practices and Infrastructure-as-Code tools

Knowledge of data governance, security, and compliance standards

Experience working in Agile/Scrum environments

Key Skills

Education

Bachelor's degree