Google is looking to solve the problem of handling information at massive scale and extending its products beyond web search, specifically with Cloud Dataproc enabling open source data analytics users to modernize their Open-Source BigData Analytics workloads in the cloud.
Requirements
- 2 years of experience with performance optimization, systems data analysis, visualization tools, or debugging.
- Experience developing with Spark, Hive, or with similar processing frameworks.
- Experience with Open-Source.
- Experience developing frameworks such as Apache Spark, Trino, or Flink.
- Knowledge of open-source big-data performance optimization problems.
Responsibilities
- Build high-impact customer-facing features which make Cloud Dataproc the best place to run Hadoop and Spark in the cloud.
- Drive technical design and execution for differentiated Performance and LakeHouse features and enhancements in an ambiguous problem space.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Enhance Apache Spark for performance, reliability, security, and monitoring, and simultaneously enhance Lake House technologies like Iceberg, Hudi, or Delta Lake for performance, security, and monitoring.
- Contribute to and adapt existing documentation or educational content based on product and program updates, as well as user feedback, while also extending open-source technologies like Apache Spark, Hive, and Trino to improve their debuggability, observability, and supportability.
Other
- Bachelor’s degree or equivalent practical experience.
- Master's degree or PhD in Computer Science or related technical field.
- Ability to work across boundaries in a distributed team.
- Must be willing to work in Sunnyvale, CA, USA or Kirkland, WA, USA
