Partner Solutions Architect, Ecosystem Engineer Data Systems

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

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.