Data Analytics Customer Engineer Google Cloud English

Google; Bengaluru; Presencial; INR 3 500 000 - 6 000 000; Tempo integral

Resumo da oferta

Google Cloud seeks an Agentic Data Cloud Customer Engineer in Bengaluru, collaborating with Sales to execute the pre-sales strategy for customers. You will map complex data estates, migrate to Google Cloud, and design end-to-end data + AI pipelines.

You will demonstrate and architect with CDOs/CTOs, building production-grade solutions that reduce cost and increase business value. You will lead hands-on demos, showcase architectures, and translate technical excellence into enterprise migrations,

Qualificações

• 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.

Responsabilidades

• 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 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 analyses and optimize architectures to prevent runaway costs on the Lightning Engine.

Conhecimentos

Data engineering

Solutions architecture

Pre-sales enablement

Python

PySpark

Data pipelines

English communication

Formação académica

Bachelor's degree or equivalent practical experience

Ferramentas

BigQuery

Apache Iceberg

Vertex AI

Spark

Knowledge Catalog

Descrição da oferta de emprego

Job Description:

Minimum qualifications:

• Bachelors 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).

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 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. If you possess deep 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.

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.

Requirements: