Python, PySpark, ETL Developer

Technology->Analytics - Packages->Python - Big Data,Technology->Big Data - Data Processing->PySpark, ETL Data Pipeline Development

• Develop and maintain scalable batch ETL pipelines using Python and PySpark for data ingestion, transformation, and loading.
• Implement reusable transformation logic, ensuring pipelines are modular, testable, and easy to maintain.
• Optimize Spark jobs for performance (partitioning, caching, joins, shuffles) and cost efficiency. Data Quality & Reliability
• Apply data validation checks, handle schema evolution, and ensure accuracy and completeness of processed datasets.
• Troubleshoot pipeline failures, analyze logs, and implement robust error handling and retry mechanisms.
• Monitor job runs and support operational stability through alerts, runbooks, and timely incident resolution. Collaboration & Delivery
• Work with cross-functional teams to gather requirements, define data mappings, and deliver datasets aligned to business needs.
• Participate in code reviews, follow engineering best practices, and contribute to continuous improvement of standards and tooling.
• Document pipeline logic, dependencies, and operational procedures for smooth handovers and long-term maintainability.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent practical experience).
• 2–5 years of hands-on experience building data pipelines using Python and PySpark.
• Strong understanding of ETL concepts, data transformations, and handling large-scale datasets.
• Proficiency in writing clean, maintainable code and debugging production issues.
• Working knowledge of data structures, algorithms, and software development best practices.