Agentic Data Cloud Customer Engineer, Startups, Google Cloud

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.
• - 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.

Minimum qualifications:

Preferred qualifications:

Qualifications

• Minimum qualifications: - 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. Preferred qualifications: - 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.