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Agentic Data Cloud Sales Specialist III, Google Cloud

Google · google.com
New York, San FranciscoSenior10+ yrs
Apply on Google’s site

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Subscribers got this job on 15 October 2026. New jobs reach them at 8 am and 6 pm, 5 days before they open here. Get the next one first

The job in short

Enterprise sales of Google Cloud data and AI platform.

SparkBigQueryGCPGenAIAgenticMarketing

Details

Found
13 d ago · posted 2026-10-15
Location as listed
New York, NY, USA
Role
Sales (other)
Pay (as listed)
$180,000–$184,000 a year
Source
The company’s own careers site

Full description

As an Agentic Data Cloud Sales Specialist, you will be at the forefront of driving Google Cloud's most critical data and AI growth. In this role, you will drive growth by securing net-new greenfield logos and identify untapped, uncommitted workloads. You will partner with Field Sales Representatives, Customer Developers, and Partners to execute joint Data+AI plays that establish long-term data platform standardization. You are a master of value-selling. You will engage with line-of-business (LoB) leaders, CDO, CAIO, and CFO to discover critical pain points, build business cases (ROI/TCO reduction), and demonstrate the quantifiable business value of modernizing data estates on GCP. Additionally, you will guide technical stakeholders and position Google Cloud's Agentic Data Cloud suite (e.g., BigQuery, Knowledge Catalog, Borderless Lakehouse, Spanner, AlloyDB) as the premium data intelligence platform for AI. You will present demos that showcase how unified, well-governed data foundations power secure, hallucination-free AI agents and real-time experiences. 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Canada: $180000 - $184000 (CAD) + 100% bonus target + equity + benefits US: $138000 - $200000 (USD) + 100% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities • Deliver against quota and achieve or exceed business and growth goals. Drive net-new greenfield logos and workloads in existing accounts through proactive campaigns. • Run complex, multi-threaded business cycles from initial discovery and value hypothesis to ROI business case creation and commercial close. • Engage Line of Business leaders, Chief Data Officer, Chief Financial Officer, Chief Marketing Officer, Chief AI Officer, through value-selling, tying modern Data and AI capabilities to quantified business outcomes. • Align cross-functionally with FSRs and Partners to execute joint Data+AI plays, driving platform standardization and co-sell motions. • Illustrate modern architectures (BigQuery, Spark, Spanner, AlloyDB) to demonstrate how unified data foundations power high-performance, hallucination-free AI agents. Minimum qualifications: • Bachelor's degree or equivalent practical experience. • 10 years of quota-carrying enterprise sales experience. • Experience building business cases through workshops or product demos to business executives. • Experience developing partnerships with technical stakeholders (e.g., Data Developers, Data Scientists). • Experience positioning and selling technical solutions in data management, analytics, infrastructure, or AI/ML. Preferred qualifications: • Experience positioning modern cloud data platforms, lakehouses, real-time streaming, or GenAI platforms against legacy or other proprietary enterprise systems. • Experience working with Global System Integrators, hyperscalers, or Independent Software Vendors (ISVs) to co-sell and accelerate pipeline. • Experience closing large-scale data and AI transformation deals within specific regulated or high-growth enterprise verticals (e.g., Financial Services, Healthcare/Life Sciences, Retail, or Manufacturing). • Practical understanding of how modern cloud architectures, data governance (e.g., cataloging, lineage, compliance), and clean data estates enable AI and real-time analytics.

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