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Agentic Data Cloud Customer Engineer III, Telco, Media, Entertainment and Gaming, Google Cloud

Google
New York City, USA
Senior · 10+ years experience
Onsite
USD 152000-221000 / year
Discovered 6 days ago

Full Job Posting

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 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 Total Cost of Ownership (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:

  • 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.
  • 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 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.
  • Practical experience implementing enterprise data/AI governance, metadata management, access control, or lineage tracking across modern catalogs.

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