Partner Solutions Architect, Ecosystem Engineer Data Systems

Partner Solutions Architect, Ecosystem Engineer Data Systems Share Partner Solutions Architect, Ecosystem Engineer Data Systems _corporate_fare_ Google _place_ Atlanta, GA, USA; Austin, TX, USA; +5 more; +4 more **Advanced** Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Share Partner Solutions Architect, Ecosystem Engineer Data Systems _info_outline_ XIn most instances, this position requires in-person interviews as part of the hiring process. The application window will be open until at least November 25, 2026. This opportunity will remain online based on business needs which may be before or after the specified date. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: **Atlanta, GA, USA; Austin, TX, USA; Boulder, CO, USA; Chicago, IL, USA; Addison, TX, USA; Miami, FL, USA** . **Minimum qualifications:**

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field, or equivalent practical experience.
• 7 years of experience in customer or partner-facing technical roles such as Solutions Architect, Partner Engineer, Customer Engineer, or Principal Data Architect in a cloud computing environment.
• Experience with enterprise data platforms, analytical data warehousing, relational and non-relational databases, and real-time data pipelines (e.g., BigQuery, Spanner, AlloyDB, Pub/Sub, Dataflow, Spark, or equivalent cloud data services).
• Experience building solutions with Large Language Models (LLMs), agentic frameworks (e.g., Google ADK, LangGraph), or Retrieval-Augmented Generation (RAG) architectures using Python, SQL, Go, or Java.

**Preferred qualifications:**

• Experience developing Model Context Protocol (MCP) servers, Agent-to-Agent (A2A) integrations, or enterprise agent plugins for Global System Integrators (GSIs) or SaaS Independent Software Vendor (ISVs).
• Experience leading cross-organizational technical programs with GSIs and translating repeatable field patterns into reusable partner code repositories and technical guides.
• Experience defining technical strategy and influencing executive leaders within global partner organizations.
• Expertise in Google Cloud's data analytics, database, and AI/ML platforms and services.
• Proven track record of migrating and modernizing complex on-premises or multi-cloud data estates (e.g., Snowflake, Databricks, Teradata, Oracle) on Google Cloud and activating them for AI workloads.

**About the job** It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll leverage Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $183000 - $265000 (USD) + 20% bonus target + equity + benefits Learn more aboutbenefits at Google ( . **Responsibilities**

• Build reusable data-to-AI solutions, architectures, and accelerators with partners on Google Cloud.
• Design and implement governed semantic layers and enterprise ontologies connecting BigQuery Graph, Conversational Analytics Agents, Looker, and Google Cloud Knowledge Catalog Custom Aspects to ground enterprise AI agents in verified business definitions.
• Validate Gemini Enterprise plugins, remote Model Context Protocol. (MCP) servers, and reusable agent skills that integrate Google Data Cloud services with third-party enterprise systems.
• Develop internal AI-assisted validation tools, evaluation rubrics, and automated architectural review workflows that scale Partner Engineering quality and turnaround velocity across partner engagements.
• Lead technical onboarding for Data and AI launches, developing implementation guides, code assets, and enablement materials.

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