Agentic Data Cloud Customer Engineer, Startups, Google Cloud

Google•Los Angeles, CA

1d•$152,000 - $221,000

About The Position

As an Agentic Data Cloud Customer Engineer, you will be 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 Chief Data Officers (CDOs), Chief Digital Officers (CTOs), lead architects, and developers. You will lead proactive discoveries to map complex, legacy customer data estates and qualify them for migration to Google Cloud Platform (GCP). You are a builder and architect that designs, codes, and deploys production-grade, end-to-end data + 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 a detailed Total Cost of Ownership (TCO) analyses and optimize architectures to prevent runaway spend. You will help customers adopt modern data engineering practices that securely unify data, analytics, and AI. With your deep expertise in Python, PySpark, and distributed data systems, you will translate technical excellence into business-transforming migrations, and 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 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: $152000 - $221000 (USD) + 42.86% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor's degree or equivalent practical experience.
10 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

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

Responsibilities

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 + 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.