Join us in shaping the future of data and AI solutions on Google Cloud Platform. This role empowers leading organizations globally by facilitating their adoption of cloud computing, making their operations more efficient, mobile, and collaborative. You'll be instrumental in solving complex technical challenges for our customers, collaborating with product and engineering teams to drive innovation and enhance Google Cloud offerings. The Google Forge Team focuses on bringing transformative goals to life for our most strategic clients, evolving enterprise data architectures into active, governed "Systems of Action" ready for agent-scale autonomy.

Google Cloud is dedicated to accelerating digital transformation across industries. We provide enterprise-grade solutions powered by Google's advanced technology, enabling developers to build more sustainably. Our trusted partnership empowers over 200 countries and territories to achieve growth and overcome critical business challenges.

Lead the end-to-end architectural design and engineering for next-generation agentic data platforms. This includes unifying operational and analytical data, building open multimodal lakehouses, and implementing semantic knowledge catalogs and graph reasoning to support autonomous AI agents.

Drive solutions from initial architecture through enterprise security, governance, and production readiness. Mentor team members and influence the strategic direction of our reusable engineering assets.

Design and develop data-centric, AI-powered solutions on GCP, demonstrating the business value of data and AI. Collaborate closely with clients to understand their unique challenges, technical needs, and strategic objectives. Establish and promote innovative best practices for data-driven solutions, contributing to industry thought leadership through publications and community engagement.

A Bachelor's degree or equivalent practical experience is required, alongside a minimum of 10 years in cloud-native architecture within a customer-facing or support capacity.

Extensive experience with Big Data technologies is essential. This includes familiarity with concepts such as analytics warehousing, data processing, data transformation, data governance, data migrations, ETL, ELT, SQL, NoSQL, performance and scalability optimizations, and batch versus streaming paradigms.

Preferred qualifications include 10 years of experience architecting and deploying large-scale distributed data platforms, cloud-native software, and AI systems. Experience with property/knowledge graphs, enterprise semantic catalogs, or multimodal data pipelines is highly valued. A proven ability to navigate enterprise InfoSec and governance processes for data and AI systems into production, implement automated LLM evaluation, semantic caching, and optimize queries for high-concurrency environments is also beneficial. A strong track record of managing multi-stakeholder technical transformations and quantifying architectural trade-offs is expected.