Elastic

Elastic

Mid Market Account Executive - Bay Area

San Francisco, CA · Mid

Sponsorship not specifiedDetected 12 days ago
ElasticsearchSalesAccount ManagementForecasting

About the role

  • Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale - unleashing the potential of businesses and people.
  • What is The Role Elastic, the Search AI company, is seeking a dynamic Field Sales Account Executive Mid Market.
  • This is an opportunity for those who are passionate about empowering companies through cutting-edge search technology and analytics, enabling them to unlock the full potential of their data.

Responsibilities

  • Drive the adoption of Elastic's AI-powered solutions by deepening our engagement with existing accounts and engaging with new groups within these customers.
  • Identify and develop new use cases, showcasing how Elastic's solutions enable users to work more efficiently and intelligently.
  • Collaborate closely with various Elastic business functions to ensure an exceptional customer experience.
  • Develop a comprehensive business plan leveraging community, customer, and partner ecosystems to drive significant growth within your territory.

Requirements

  • A proven track record in SaaS subscription sales, particularly with cross sell motions within accounts, evidenced by quota overachievement and strong customer references.
  • In-depth understanding and, ideally, experience in selling solutions related to Enterprise Search, Log Analytics, Security, APM.
  • Experience in selling within an Open Source model.
  • Adept at building relationships and establishing credibility with both developers and executives.
  • Consistent and accurate sales forecasting skills using SFDC.
  • Enthusiasm for the Open Source model and a deep appreciation for the community relying on our solutions.
  • Prior experience selling into the Enterprise accounts included in this territory.
  • Bonus Points
  • Compensation for this role is in the form of base salary plus a variable component, that together comprise the On-Target Earnings (OTE). On-Target Earnings (OTE) are based on a 50/50 pay mix (base salary / target variable).
  • The typical starting OTE range for new hires in this role is listed below. This range represents the lowest to highest OTE we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future.
  • An employee's position within the OTE range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.
  • Elastic believes that employees should have the opportunity to share in the

Compensation

  • for this role is in the form of base salary plus a variable component, that together comprise the On-Target Earnings (OTE).
  • On-Target Earnings (OTE) are based on a 50/50 pay mix (base salary / target variable).
  • The typical starting OTE range for new hires in this role is listed below.
  • This range represents the lowest to highest OTE we reasonably and in good faith believe we would pay for this role at the time of this posting.
  • We may ultimately pay more or less than the posted range, and the range may be modified in the future.
  • An employee's position within the OTE range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.

Company info

  • Position yourself as a trusted advisor, assisting users and customers in harnessing the full power of Elastic's search analytics to transform their data into actionable insights.
  • Proactively identify new business opportunities with customers, successfully navigating complex sales cycles.
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This listing is sourced directly from Elastic's careers page and normalized into a canonical job model.