Key Responsibilities :

- Design, develop, and maintain scalable data processing pipelines using Apache Spark and PySpark.

- Develop efficient data transformation and processing solutions using Python and SQL.

- Work with large volumes of structured and unstructured data to deliver high-performance data solutions.

- Optimize Spark jobs for performance, scalability, and reliability.

- Perform data cleansing, validation, transformation, and aggregation processes.

- Develop and maintain ETL/ELT workflows to support enterprise data platforms.

- Write complex SQL queries for data extraction, analysis, and optimization.

- Implement Spark optimization techniques including partitioning, caching, and performance tuning.

- Collaborate with data architects, analysts, and business teams to deliver data-driven solutions.

- Troubleshoot data pipeline issues and ensure data quality and consistency.

- Prepare technical documentation and follow data engineering best practices.

Required Skills :

- Strong programming experience in Python for data engineering applications.

- Hands-on experience with PySpark and Apache Spark ecosystem.

- Strong SQL coding skills with experience writing complex queries and optimizing SQL performance.

- Good understanding of Spark architecture, execution model, and optimization techniques.

- Experience developing scalable batch and data processing applications.

- Strong knowledge of data structures, data processing concepts, and distributed computing.

- Experience with ETL/ELT development and data pipeline implementation.

- Understanding of data warehousing concepts and data modeling.

- Experience working with large datasets and performance optimization.

- Strong analytical, debugging, and problem-solving skills.