Spark Engineer
Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications for petabyte-scale data processing.
🚀 Master Apache Spark for processing massive datasets across distributed clusters. Build high-performance ETL pipelines using DataFrames and Spark SQL, optimize resource usage through smart partitioning and caching, and handle petabyte-scale data processing with production-grade reliability.
💡 Perfect for transforming large data volumes, streaming real-time analytics, optimizing slow pipelines, migrating legacy systems, and troubleshooting performance bottlenecks. Whether you're building data warehouses or processing complex transformations, this skill delivers scalable solutions.
✨ Get expert guidance on tuning configurations, eliminating data skew, designing efficient joins, and monitoring Spark UI metrics—ensuring your applications run at peak performance while minimizing costs.
Requirements
Apache Spark distributed computing framework (2.4.0+)
Scala or Python runtime for Spark application development
Hadoop ecosystem for distributed storage and cluster management