Software Engineer, Managed Service for Apache Spark

Software Engineer, Managed Service for Apache Spark corporate_fare Google place Kirkland, WA, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. info_outline X In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees.

Benefits for this role include:

Health, dental, vision, life, disability insurance

Retirement Benefits:

401(k) with company match

Paid Time Off:

20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment

Sick Time:

40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks

Baby Bonding Leave:

18 weeks

Holidays:

13 paid days per year Minimum qualifications: Bachelor's degree or equivalent practical experience. 2 years of experience with software development, or 1 year of experience with an advanced degree in an industry setting.

Preferred qualifications:

Master's degree or PhD in Computer Science, or related technical fields. 2 years of experience with data structures and algorithms. Experience with Apache Spark, Apache YARN, Hadoop Distributed File System, and Compute Engine. 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. Managed Service for Apache Spark enables open source data analytics users (Apache Hadoop, Spark, Presto, Flink, etc.) to lift and modernize their workloads into the cloud. It is a fast, easy-to-use, fully managed cloud service for running Apache Spark, Apache Hadoop and dozens of other OSS softwares in a simpler, performant and cost-efficient way. Operations that take hours or days now complete in seconds or minutes, and the customer pays only for the resources they use (with per-second billing). Managed Service for Apache Spark also easily integrates with other Google Cloud Platform (GCP) services like BigQuery, Dataplex (governance, lineage), Catalogs/Meta stores to give a powerful and complete platform for data processing, analytics, and machine learning. 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 $147,000-$211,000 + 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 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 . Responsibilities Design and implement features that make Dataproc easier to use and and the most efficient way to run Spark workloads. Architect, implement, and operate the core Dataproc Control Plane, focusing on high-availability, fault-tolerance, and state management for global-scale resource provisioning and cluster orchestration. Build high-impact customer-facing features which make Dataproc the best place to run Hadoop, Spark, Flink, AI/ML and newer technologies in the cloud.