Job Description

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 integ...

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