**Overview**

**What You’ll Achieve:**

+ Design, develop, and maintain scalable data processing solutions across on-premises and cloud environments using Python and Apache Spark.
+ Optimize and fine-tune Spark jobs for performance, including resource utilization, shuffling, partitioning, and caching, to ensure maximum efficiency in large-scale big data environments.
+ Design and implement scalable, fault-tolerant data pipelines with end-to-end monitoring, alerting, and logging.
+ Leverage AWS cloud services (2+ years preferred) to build and manage data pipelines and distributed processing workloads.
+ Develop and optimize SQL queries across relational and data warehouse systems (RDBMS/Data Warehouse).
+ Apply design patterns and best practices for efficient data modeling, partitioning, and distributed system performance.
+ Use Git (or equivalent) for source control and maintain strong unit and integration testing practices.
+ Collaborate with Produ...