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X Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Austin, TX, USA; New York, NY, USA; San Jose, CA, USA.
Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing and launching software products.
- Experience designing and implementing large-scale data architecture frameworks, enterprise data warehouses/data lakes, or production-grade AI/ML pipelines.
Preferred qualifications:- 10 years of experience in technology, software engineering, or enterprise architecture, with a heavy emphasis on data engineering or AI/ML systems.
- Experience influencing decentralized technical teams and drive the concrete adoption of centralized engineering standards without direct authority.
- Experience developing data and product strategies across a diverse portfolio of internal systems within a complex, large-scale enterprise.
About the jobIn this role, you will be responsible for defining, driving, and scaling enterprise-wide architectural standards, reference models, and patterns that empower Corporate Engineering to build innovative, secure, and data-driven capabilities.
At Corp Eng, we build world-leading business solutions that scale a more helpful Google for everyone. As Google's IT organization, we provide end-to-end solutions for organizations across Google. We deliver the right tools, platforms, and experiences for all Googlers as they create more helpful products and services for everyone. In the simplest terms, we are Google for Googlers.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target bonus equity benefits
Learn more about benefits at Google .
Responsibilities - Design, develop, and maintain enterprise-grade reference architectures, blueprints, and technical roadmaps for next-generation data platforms and AI/ML capabilities across Corporate Engineering.
- Conduct deep-dive technical design consultations and architecture reviews to ensure emerging product strategies align with long-term data scalability and enterprise AI safety goals.
- Support the Enterprise Architecture Board (EAB) by reviewing high-impact data and AI proposals, proactively identifying architectural fragmentation, and mitigating security threats or technical debt.
- Lead technical forums, architecture deep-dives, and workshops to elevate the data engineering and AI competencies of technical leads and builders across Corporate Engineering.
- Establish reusable architectural patterns, templates, and principles for data ingestion, pipeline orchestration, model deployment, and generative AI integrations to reduce systemic redundancy and accelerate delivery.
