Build and scale data solutions that power smarter decisions. In this role, you’ll work at the intersection of software engineering and data engineering—using Python, PySpark, and ETL to transform raw, complex datasets into reliable, analytics-ready assets. You’ll collaborate closely with data engineers, analysts, and stakeholders to understand requirements, design efficient pipelines, and deliver high-quality outputs on time. If you enjoy solving performance challenges, improving data quality, and creating maintainable code that runs in production, this is a great opportunity to grow your impact. Expect a supportive, collaborative environment where ownership is encouraged, learning is continuous, and your contributions directly improve how teams access and trust data. 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.