Job description

As an Agentic Data Cloud Customer Engineer, you are the premier technical subject matter expert in your territory. You will partner with Sales Specialists to execute the technical pre-sales strategy for customers, serving as the trusted technical advisor to C-levels, Lead Architects, and Developers. You will lead proactive discoveries to map complex, legacy customer data estates and qualify them for migration to GCP. You are a builder and architect that designs, codes, and deploys production-grade, end-to-end Data and AI pipelines that solve real-world enterprise problems. You will whiteboard modern open lakehouse architectures and run code demonstrations showing how unified data foundations power deterministic, hallucination-free conversational AI. Leveraging a background in value-selling, you will conduct detailed Total Cost of Ownership analyses and optimize architectures to prevent runaway spend. You will help customers adopt modern data engineering practices that securely unify data, analytics, and AI. You will use your expertise in Python, PySpark, and distributed data systems, and can translate technical excellence into business-transforming migrations, you will grow in this highly technical role. 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: $127000 - $184000 (USD) + 42.86% bonus target + equity + benefits Learn more about benefits at Google . • Define technical strategy for large accounts, engaging C-level executives via live demonstrations to solve business problems using the Agentic Data Cloud. • Design, code, and deploy production-grade Data and AI pipelines (e.g., fraud detection, recommendation engines) and map legacy data estates to open-format architectures (Apache Iceberg). • Orchestrate intent-driven data engineering to autonomously deploy PySpark/dbt pipelines using the Data Agent Kit, Antigravity, and Model Context Protocol. • Lead high-impact showcases and technical value validations using BigQuery, Borderless Lakehouse, Knowledge Catalog, Spark, and Vertex AI to prove business outcomes. • Conduct detailed TCO analyses and optimize architectures (such as balancing DRAM/SSD, compute shapes, and token spend) to prevent runaway costs on the Lightning Engine. • Bachelor's degree or equivalent practical experience. • 6 years of experience as a data or systems engineer, solutions architect, or pre-sales consultant. • Experience delivering demos, workshops, or architect overviews to business leaders. • Experience migrating, refactoring, or debugging proprietary or open source workloads. • Experience building data platforms, warehouses, or data lakes, and experience with data programming languages and leveraging agentic platforms. Minimum qualifications: • Bachelor's degree or equivalent practical experience. • 6 years of experience as a data or systems engineer, solutions architect, or pre-sales consultant. • Experience delivering demos, workshops, or architect overviews to business leaders. • Experience migrating, refactoring, or debugging proprietary or open source workloads. • Experience building data platforms, warehouses, or data lakes, and experience with data programming languages and leveraging agentic platforms. Preferred qualifications: • Experience designing and deploying enterprise Retrieval-Augmented Generation (RAG) pipelines, LLM application integrations, or context orchestration frameworks. • Experience implementing enterprise data/AI governance, metadata management, access control, or lineage tracking across modern catalogs. • Experience with developer advocacy, building internal technical advocate programs, delivering technical enablement, or contributing to open-source data/AI communities. • Experience in core data science workflows, including proficiency with data manipulation libraries and integrating data pipelines with ML platforms. • Experience applying value-selling principles to align technical architectures with business outcomes.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Define technical strategy for large accounts, engaging C-level executives via live demonstrations to solve business problems using the Agentic Data Cloud.
Design, code, and deploy production-grade Data and AI pipelines (e.g., fraud detection, recommendation engines) and map legacy data estates to open-format architectures (Apache Iceberg).
Orchestrate intent-driven data engineering to autonomously deploy PySpark/dbt pipelines using the Data Agent Kit, Antigravity, and Model Context Protocol.
Lead high-impact showcases and technical value validations using BigQuery, Borderless Lakehouse, Knowledge Catalog, Spark, and Vertex AI to prove business outcomes.
Conduct detailed TCO analyses and optimize architectures (such as balancing DRAM/SSD, compute shapes, and token spend) to prevent runaway costs on the Lightning Engine.
Bachelor's degree or equivalent practical experience.
6 years of experience as a data or systems engineer, solutions architect, or pre-sales consultant.
Experience delivering demos, workshops, or architect overviews to business leaders.
Experience migrating, refactoring, or debugging proprietary or open source workloads.
Experience building data platforms, warehouses, or data lakes, and experience with data programming languages and leveraging agentic platforms.
Bachelor's degree or equivalent practical experience.
6 years of experience as a data or systems engineer, solutions architect, or pre-sales consultant.
Experience delivering demos, workshops, or architect overviews to business leaders.
Experience migrating, refactoring, or debugging proprietary or open source workloads.
Experience building data platforms, warehouses, or data lakes, and experience with data programming languages and leveraging agentic platforms.

Nice-to-haves

US: $127000 - $184000 (USD) + 42.86% bonus target + equity + benefits
Experience designing and deploying enterprise Retrieval-Augmented Generation (RAG) pipelines, LLM application integrations, or context orchestration frameworks.
Experience implementing enterprise data/AI governance, metadata management, access control, or lineage tracking across modern catalogs.
Experience with developer advocacy, building internal technical advocate programs, delivering technical enablement, or contributing to open-source data/AI communities.
Experience in core data science workflows, including proficiency with data manipulation libraries and integrating data pipelines with ML platforms.
Experience applying value-selling principles to align technical architectures with business outcomes.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gcpaipythonllmml

Platform: google
Effort: medium