Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 5 years of experience designing, analyzing and troubleshooting large-scale distributed systems.
- 3 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.
Preferred qualifications:- Master's degree or PhD in Computer Science or a related technical field.
- Experience developing frameworks such as Apache Spark, Trino, or Flink.
- Knowledge of open-source big-data performance optimization problems.
- Ability to work across boundaries in a distributed team.
About the jobCloud Dataproc enables open source data analytics users (e.g., Apache Hadoop, Spark, Flink, Trino, etc.) to modernize their Open-Source BigData Analytics workloads in the cloud. Dedicated to meeting customers where they are, Dataproc enables users to quickly provision and manage clusters and workloads.
The US base salary range for this full-time position is $174,000-$252,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities - Build 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.
- 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.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
