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.
Lead the design, development, and iterative refinement of data-centric and AI-powered solutions on Google Cloud Platform (GCP), showcasing the potential of data and AI to address specific business needs.
Collaborate closely with customers to understand their business challenges, technical requirements, and strategic objectives. Build strong, trusted relationships with customers and stakeholders.
Mentor and guide junior team members, fostering a culture of innovation and continuous learning.
Establish and promote innovative best practices and methodologies for data-motivated solutions, actively contributing to industry thought leadership through publications, presentations, and community engagement.
Minimum qualifications
Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
10 years of experience in cloud computing, with a focus on data architecture, data analytics, and data engineering.
Experience in programming/scripting languages (e.g., Python, Spark), and developing and deploying data solutions.
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Preferred qualifications
10 years of experience architecting and delivering large-scale distributed data platforms, cloud-native software, and AI-driven systems in a customer-facing engineering, principal architecture, or technical consulting role.
Experience designing property/knowledge graphs (GQL, Cypher, RDF/OWL), enterprise semantic catalogs, or multimodal data pipelines that process and index unstructured data alongside tabular data.
Real-world experience taking data and AI systems through enterprise InfoSec and governance sign-off into production, implementing automated LLM/agent evaluation harnesses, semantic caching, and compute/query optimization for high-concurrency workloads.
Proven track record of owning multi-stakeholder technical transformations, quantifying architectural trade-offs and building consensus with executives.
Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
10 years of experience in cloud computing, with a focus on data architecture, data analytics, and data engineering.
Experience in programming/scripting languages (e.g., Python, Spark), and developing and deploying data solutions.
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
