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Data Cloud Sales Specialist III, Cloud

Google
Atlanta, USA
Senior · 10+ years experience
Onsite
USD 138000-200000 / year
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Responsibilities

  • Deliver against quota and achieve or exceed strategic 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.
  • Whiteboard 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., Enterprise Architects, Data Engineers, 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 ISVs to co-sell and accelerate pipeline.
  • Experience managing large-scale data and AI transformation agreements 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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