Responsibilities
- Deliver strategic business outcomes and growth goals while achieving or exceeding your sales quota. Drive net-new greenfield logos and new workloads in existing accounts through proactive engagement.
- Run complex, multi-threaded sales cycles from initial discovery and value hypothesis to ROI business case creation and commercial close.
- Engage C-level, Lines of Business and IT teams, through value-selling, tying modern Data and AI capabilities to quantified business outcomes.
- Align cross-functionally with Field Sales Representatives (FSRs) and Partners to execute joint Data and AI sales plays to scale success and GCP data adoption, driving platform standardization and co-sell motions.
- Whiteboard end-to-end modern data architectures including but not limited to BigQuery, Spark, Spanner and AlloyDB to demonstrate how unified data foundations power optimized -price-performance, hallucination-free AI agents.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience with quota-carrying enterprise sales.
- 5 years of experience leading enterprise data modernization, cloud data warehousing (e.g., Online Analytical Processing (OLAP)), or data platform migrations.
- 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 Generative AI (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 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.