Must-Have** • Strong proficiency in Python programming. • Hands-on experience with PySpark and Apache Spark. • Knowledge of Big Data technologies (Hadoop, Hive, Kafka, etc.). • Experience with SQL and relational/non-relational databases. • Familiarity with distributed computing and parallel processing. • Understanding data engineering best practices. • Experience with REST APIs, JSON/XML, and data serialization. • Exposure to cloud computing environments. • 5+ years of experience in Python and PySpark development. • Experience with data warehousing and data lakes. • Knowledge of machine learning libraries (e.g., MLlib) is a plus. • Strong problem-solving and debugging skills. • Excellent communication and collaboration abilities. • Develop and maintain scalable data pipelines using Python and PySpark. • Design and implement ETL (Extract, Transform, Load) processes. • Optimize and troubleshoot existing PySpark applications for performance. • Collaborate with cross-functional teams to understand data requirements. • Write clean, efficient, and well-documented code. • Conduct code reviews and participate in design discussions. • Ensure data integrity and quality across the data lifecycle. • Integrate with cloud platforms like AWS, Azure, or GCP. Implement data storage solutions and manage large-scale datasets.