Job description

As a Technical Solutions Consultant, you will be responsible for the technical relationship of our largest advertising clients and/or product partners. You will lead cross-functional teams in Engineering, Sales and Product Management to leverage emerging technologies for our external clients/partners. From concept design and testing to data analysis and support, you will oversee the technical execution and business operations of Google's online advertising platforms and/or product partnerships. You will be able to balance business and partner needs with technical constraints, develop innovative, cutting edge solutions and act as a partner and consultant to those you are working with. You will also be able to build tools and automate products, oversee the technical execution and business operations of Google's partnerships, as well as develop product strategy and prioritize projects and resources. As a Solutions Architect for Data Cloud, you will act as a technical expert for Google Cloud's Customer Engineering and field teams. You’ll in-depth search into complex customer issues, build highly secure, scalable reference implementations, and advocate our technologies across the ecosystem. You’ll be responsible for technical architecture spanning on-prem and cloud-based solutions introducing novelty adjustments and AI builds to support the Agentic era. In your technical solutioning you will take into account factors like cost, security, privacy, regulatory requirements, data governance, and reliability in the design. By bridging Customer Engineering, Product, and Engineering, you will eliminate friction, accelerate deployment, and help our field teams and customers build what's next at lightning speed. 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $233000 - $324000 (USD) + 25% bonus target + equity + benefits Learn more about benefits at Google . • Design, prototype, and publish production-ready Data Cloud assets, reference architectures, and technical accelerators to scale field enablement. • Establish end-to-end technical ownership over complex data domain spaces, guiding core architectural decisions across databases, analytics, business intelligence and GenAI. • Architect and scale high-impact, technical assets (demos, technical reference architectures, and guides) that showcase Google Cloud's data capabilities to address our customers' toughest issues. • Collaborate closely with Product and Engineering to inject real-world insights directly into the product roadmap, driving continuous innovation. • Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience. • 10 years of experience with ETL tools, data pipeline orchestration (e.g., Spark), and data warehousing architectures. • 2 years of experience with Google Cloud’s Vertex Platform. • Experience with SQL, NoSQL, Python, Java, or data modeling techniques. Minimum qualifications: • Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience. • 10 years of experience with ETL tools, data pipeline orchestration (e.g., Spark), and data warehousing architectures. • 2 years of experience with Google Cloud’s Vertex Platform. • Experience with SQL, NoSQL, Python, Java, or data modeling techniques. Preferred qualifications: • Master's degree in Computer Science, related engineering field, or equivalent practical experience. • 15 years of experience writing bespoke code, building custom tooling, or contributing to core codebases to resolve critical technical issues. • Experience developing, deploying, and optimizing data architecture deployments and generative AI agents, particularly conversational AI on enterprise platforms. • Expertise in CI/CD pipelines, agent performance tuning, and production readiness for cloud-based data applications. • Proven track record of creating reusable tools, technical accelerators, and documentation to scale engineering enablement.

Requirements

Must-haves

Learn more about benefits at Google .
Design, prototype, and publish production-ready Data Cloud assets, reference architectures, and technical accelerators to scale field enablement.
Establish end-to-end technical ownership over complex data domain spaces, guiding core architectural decisions across databases, analytics, business intelligence and GenAI.
Architect and scale high-impact, technical assets (demos, technical reference architectures, and guides) that showcase Google Cloud's data capabilities to address our customers' toughest issues.
Collaborate closely with Product and Engineering to inject real-world insights directly into the product roadmap, driving continuous innovation.
Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
10 years of experience with ETL tools, data pipeline orchestration (e.g., Spark), and data warehousing architectures.
2 years of experience with Google Cloud’s Vertex Platform.
Experience with SQL, NoSQL, Python, Java, or data modeling techniques.
Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
10 years of experience with ETL tools, data pipeline orchestration (e.g., Spark), and data warehousing architectures.
2 years of experience with Google Cloud’s Vertex Platform.
Experience with SQL, NoSQL, Python, Java, or data modeling techniques.
15 years of experience writing bespoke code, building custom tooling, or contributing to core codebases to resolve critical technical issues.

Nice-to-haves

US: $233000 - $324000 (USD) + 25% bonus target + equity + benefits
Master's degree in Computer Science, related engineering field, or equivalent practical experience.
Experience developing, deploying, and optimizing data architecture deployments and generative AI agents, particularly conversational AI on enterprise platforms.
Expertise in CI/CD pipelines, agent performance tuning, and production readiness for cloud-based data applications.
Proven track record of creating reusable tools, technical accelerators, and documentation to scale engineering enablement.

Application prep

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Keywords to mirror

aisqlpythonjavaci/cd

Platform: google
Effort: medium