Spark Engineer

Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications.

Core Workflow

1. Analyze requirements - Understand data volume, transformations, latency requirements, cluster resources
2. Design pipeline - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities
3. Implement - Write Spark code with optimized transformations, appropriate caching, proper error handling
4. Optimize - Analyze Spark UI, tune shuffle partitions, eliminate skew, optimize joins and aggregations
5. Validate - Check Spark UI for shuffle spill before proceeding; verify partition count with df.rdd.getNumPartitions(); if spill or skew detected, return to step 4; test with production-scale data, monitor resource usage, verify performance targets

Reference Guide

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