Like Google's own ambitions, the work of a Software Engineer goes way beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of engineers. You not only optimize your own code but make sure engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
BigQuery's Advanced Analytics team is responsible for the AI, ML, Search, and core extensibility architecture of BigQuery, with a mission to make BigQuery scalable and easy to use data platform for AI/ML analytics and search, powering the applications and agents of the AI age.
We achieve this goal by building scalable primitives within the BigQuery engine, combined with an extensibility architecture to bridge the gap between traditional data warehousing and the modern data lake by allowing customers to execute custom logic in the languages and frameworks they prefer.
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 $207,000-$300,000 + 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
- Develop and execute a multi-year technical roadmap for BigQuery's extensibility platform, enabling developers and data scientists to run custom logic (such as Python and JavaScript) directly within the data platform. A core focus is AI/ML pre-processing, training, and inference.
- Lead the end-to-end lifecycle of high-impact features—including programmable functions and remote compute integrations—from initial design and preview through to global general availability and enterprise-scale adoption.
- Drive the technical vision for expanding the engine's capabilities to support hybrid compute workloads, including the native integration of large-scale batch processing and distributed computing frameworks.
- Oversee the production stability and performance of the extensibility layer, balancing the rapid delivery of new innovations with the continuous improvement of core infrastructure and system health.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 3 years of experience building and developing large-scale infrastructure or distributed systems.
- 3 years of experience in a technical leadership role, overseeing projects.
- 2 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- Experience with the Python data science ecosystem (e.g., Pandas, scikit-learn, BigFrames) and building seamless integrations between SQL engines and AI/ML frameworks.
- Direct experience with distributed data processing frameworks (e.g., Apache Spark, Flume, Beam, or Dataflow), particularly for batch processing and data migration.
- Domain expertise in database and data analytics systems, specifically query processing, runtime technologies (e.g., vectorized execution), and large-scale data warehouse internals.