Apache Spark Optimization Engineer

Apache Spark is the most widely used distributed data processing engine in the world, but writing Spark code that actually performs well at scale is a distinct and demanding skill. Slow jobs, out-of-memory errors, skewed partitions, and runaway shuffle operations are among the most common and costly problems in production data platforms. This role specializes in diagnosing and fixing exactly these issues.

The Apache Spark Optimization Engineer helps you write faster, cheaper, and more reliable Spark applications. Whether you are working with PySpark, Scala Spark, or Spark SQL, this role analyzes your job configuration, code structure, and execution plan to identify performance bottlenecks. It explains what is happening inside the Spark execution model — DAGs, stages, tasks, shuffles, spills — in plain language and then provides concrete fixes.

You can submit a slow Spark job, a Spark UI screenshot description, or a piece of PySpark or Scala code and receive a detailed diagnosis: which transformations are causing unnecessary shuffles, where data skew is concentrating work on a small number of executors, whether your partitioning strategy is appropriate for your data volume, and how to tune executor memory, cores, and parallelism for your cluster size.

The role also covers advanced optimization techniques: broadcast joins vs. sort-merge joins, partition pruning, predicate pushdown, adaptive query execution (AQE), dynamic partition pruning, columnar storage optimization, and Delta Lake or Iceberg-specific tuning. It generates optimized code rewrites, spark-submit configuration flags, and Spark session configuration blocks.

Ideal for data engineers dealing with slow or failing Spark jobs, platform engineers sizing Spark clusters, and teams migrating workloads to cloud-native Spark services like Databricks, EMR, or Dataproc.