Spark Product Manager

IBM

Quick summary

Work type
On-site
Location
San Jose, CA
Posted
41 days ago

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Similar $191k
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Listed pay typically runs $1,000,000–$1,000,000 across 8 roles with salary data.

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TL;DR · Spark Product Manager

We are seeking a Senior Product Manager to lead critical initiatives within our Apache Spark engine team in San Jose. This role involves driving the roadmap for key components such as the Catalyst optimizer and lakehouse integrations with Delta Lake, Iceberg, and Hudi. You will work closely with platform engineers, data engineering teams, and ML infrastructure groups to optimize query performance, reduce costs, and enhance scalability through innovations like dynamic partition pruning and shuffle optimization. The ideal candidate has deep expertise in Spark internals, JVM performance tuning, and open-source community dynamics, along with experience in structured streaming and observability features. This position requires a strong quantitative background and the ability to translate complex technical insights into actionable product improvements.

What you'll do

  • Drive the roadmap for Apache Spark engine improvements including Catalyst optimizer and dynamic partition pruning.
  • Define integration strategies for Spark with Delta Lake, Iceberg, and Hudi for read/write optimization.
  • Lead investments in Structured Streaming features like watermarking and stateful processing at scale.
  • Partner with infrastructure teams to optimize shuffle operations and reduce query costs.
  • Ensure cross-functional alignment among data engineers, SREs, and ML teams on Spark engine investments.

What we're looking for

  • Extensive experience driving Apache Spark engine roadmap and optimizations.
  • Deep understanding of lakehouse integrations with Delta Lake, Iceberg, Hudi.
  • Expertise in Structured Streaming, including stateful processing at scale.
  • Proven ability to partner on cost-efficiency initiatives like shuffle optimization.
  • Familiarity with JVM performance tuning and native acceleration tradeoffs.
  • Working knowledge of Spark internals: DAG scheduler, Catalyst optimizer.
  • Experience contributing to Apache Spark or major Spark distributions.

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