Data Analytics Customer Engineer, Google Cloud (English)

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.
  
• Ability to communicate in English fluently to manage stakeholder relationships.

Preferred qualifications:

• Experience designing and deploying enterprise Retrieval-Augmented Generation (RAG) pipelines, Large Language Model (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 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.

About the job

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 CDOs, 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).

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 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.