Agentic Data Cloud Architect, APAC, Google Cloud

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business - all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers - developers, small and large businesses, educational institutions and government agencies - see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

The Google Cloud APAC Agentic Data Cloud Architect team is a specialized team that guides customers in choosing, designing and accelerating adoption of Google Cloud’s unified Data Cloud and Artificial Intelligence (AI) capabilities. The team bridges the gap between our powerful platform primitives across data analytics, databases and storage, and the cohesive, high-value solutions enterprises demand. This group operates as a customer-zero laboratory. The team embeds deeply with a select group of lighthouse customers to co-develop breakthrough solutions in real-world environments. The team’s primary mandate is to translate these engagements into scalable assets, delivering validated product prototypes to engineering and repeatable go-to-market blueprints to the field.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.

Responsibilities

• Serve as the primary technical partner to customer Chief Technology Officers (CTOs) and chief architects, leading high-stakes workshops to align their technology roadmap with Google’s Data and AI strategy.
• Act as principal architect for Google’s Agentic Data Cloud, setting engineering standards, winning complex workloads, and accelerating adoption across lighthouse engagements from evaluation through ramp.
• Capture repeatable patterns to build scalable Golden Path blueprints, reference architectures, and field-ready assets that drive broad adoption across thousands of customers.
• Synthesize customer friction points and cross-product gaps into authoritative engineering requirements to shape and protect Google's unified Data and AI narrative.
• Mentor and guide subregional Datacloud Customer Engineers, remaining experience for critical architectures while directing the team to deliver secure, scalable solutions.

Qualifications

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with delivering data and analytics solutions in a customer-facing or support role.
• Experience in building and deploying enterprise Generative AI applications and agentic workflows integrated with modern data platforms.
• Experience in architecting distributed data systems (e.g., Lakehouse, Data Mesh), batch and streaming pipelines (e.g., Spark, Flink, BigQuery, Iceberg), and transactional databases (e.g., PostgreSQL, MySQL, NoSQL databases).
• Experience in cloud-native data warehousing, ETL/ELT pipelines, real-time event streaming, governance, and end-to-end migrations.
• Experience partnering with, influencing, and presenting architectures to executive leaders and technical stakeholders.

Preferred qualifications:

• Experience interacting with C-level executives (e.g., CTO, Chief Data Officer (CDO), Chief Information Officer (CIO)) to translate complex business requirements into technical strategies via simple, compelling narratives including organizational, communication, and leadership skills, with the ability to prioritize and manage across multiple time sensitive initiatives.
• Knowledge of emerging agentic technologies and their implications for data architecture.
• Proficiency in writing, refactoring, and debugging Python, PySpark, and SQL natively to support high-performance data engineering, ML frameworks (e.g., PyTorch, TensorFlow), and troubleshooting workloads.
• Technical expertise in designing distributed systems, data platforms, and AI/ML architectures for enterprise scale.