Senior Software Engineering Manager, Dataproc, Google Cloud

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.

With your extensive technical expertise you take initiative to independently design and implement new systems, designing, implementing, and testing multiple features with little or no direction from tech lead or manager. You collaborate with key stakeholders to determine future direction of work.

Google Cloud Dataproc delivers fully managed Apache Spark, Flink, and Trino at planetary scale.

In this role, you will build an AI-ready platform with native GPU support and specialized runtimes (PyTorch, TensorFlow) integrated with Vertex AI. You will help us define the unified open lakehouse of tomorrow—bridging Apache Iceberg, Delta, and Hudi with enterprise-grade security.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $365000 (USD) + 25% bonus target + bonus + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Provide strategic leadership and goal for a high-performing engineering organization, driving the roadmap for Spark and next-generation lake house architectures.
• Architect global-scale data lake and lake house solutions, integrating open-source standards like Iceberg and Delta Lake with internal BigQuery ecosystems.
• Foster a culture of technical innovation and excellence, optimizing open-source stacks for peak performance, security, and enterprise-grade efficiency. Collaborate with cross-functional GCP infrastructure and product teams to deliver seamless, observability-rich platforms for global enterprise customers.
• Direct the integration of AI, Machine Learning (ML), and data science workloads into the core dataproc and lake house offerings. Manage stakeholder relationships and influence internal and external open-source communities to align with Google's strategic objectives.
• Oversee organizational health, talent development, and resource allocation across multiple distributed teams to ensure operational excellence and high engagement.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience with software development.
• 5 years of experience in a technical leadership role.
• 5 years of experience in a people management or team leadership role.
• Experience with Apache Spark, data processing, and data analytics.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 10 years of experience with executive managers.
• Experience in using Google or other Cloud technologies, building highly scalable distributed systems.
• Knowledge of open source data analytics technologies such as Apache YARN, Spark, Hive, Flink, etc.
• Knowledge of data lakes/lake houses like Iceberg, Delta, BQ Iceberg tables.