DTP Designer

Python, PySpark, ETL Developer

Technology->Analytics - Packages->Python - Big Data,Technology->Big Data - Data Processing->PySpark, ETL • 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. 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.