Software Engineer, Cloud Dataproc, Open Source

Minimum Qualifications:

• Bachelor’s degree or equivalent practical experience.
• 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.

Preferred Qualifications:

• Master's degree or PhD in Computer Science or 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 Job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Cloud Dataproc enables open source data analytics users (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.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $141,000-$202,000 + 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.
The US base salary range for this full-time position is $141,000-$202,000 + 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 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.