Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.
• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
• Experience in backend development, infrastructure design, and distributed processing.

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience programming in C and building large-scale data processing systems.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Ads Quality Infrastructure is the core infrastructure powering Google's Search Ads. In this role, you will manage the core data platform team. You will focus on three foundational pillars:

• Core Data Platform: The engine for extracting, transforming, and propagating Ads data, hosting streams and pipelines, and serving pipelines under strict availability and latency service level agreement (SLAs).
• LLM Workflow Integration: Hosted infrastructure to integrate LLM workflows with the infrastructure stack for content understanding and generation.
• Data Observability: To provide visibility into pipeline data, lineage, debugging, and business and infrastructure metrics.

You will also have the opportunity to shape critical Ads data infrastructure.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We're made up of multiple teams, building Google's Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) 25% bonus target equity benefits

Learn more about benefits at Google .

Responsibilities

• Define the architectural direction and unified strategy for the core data platform, and collaborate with clients and partner teams.
• Scale distributed systems to process data growth, managing real-time updates at peak rates of 1 million database row changes per second.
• Build client-facing APIs, data abstractions, and data pipelines for client teams.
• Maintain latency service level objective (SLOs) and system availability while isolating customer environments and managing large-scale customer datasets.
• Provide technical mentorship to engineers across the team and establish engineering practices.