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Monte Carlo Data

End-to-end data observability and incident resolution

Strategic Account Executive

Account ExecutiveUpdated on Nov 7
Full-timeLondon, UK

About the Job

Requirements

  • At least 7 years of SaaS experience, including at least 5 years in closing roles.
  • Experience selling to Global 2000 companies.
  • Experience engaging with C-suite executives.
  • A demonstrated track record in an early-stage company or highly ambiguous environment.
  • Experience selling complex and technical products to data and engineering teams.
  • Experience in at least two of outbound sales, category creation, and build-versus-buy selling.
  • A proven track record of closing six- and seven-figure software cloud deals with prospects and customers.
  • Experience with target account selling, solution selling, or consultative sales techniques.

Responsibilities

  • Develop and execute consultative and solution sales strategies and tactics to generate pipeline, drive sales opportunities, and deliver repeatable and predictable bookings.
  • Use account-based marketing support to prospect to CTOs and data leaders.
  • Lead discovery to gather Business Value Assessment inputs.
  • Co-own the development of Business Value Assessment presentations and present them to prospects.
  • Build thought leadership and own executive buyer relationships, defining positive business outcomes for large enterprises.
  • Collaborate with internal functional areas, including sales engineering, marketing, sales, and partnerships, and with external partners and customers.
  • Research, identify, and generate new business opportunities, and build and manage a sales funnel and pipeline.

Desired Qualifications

  • Experience with consumption models, or willingness to learn.
  • Knowledge of MEDDPICC and Challenger methodologies.

About the Company

Monte Carlo Data

End-to-end data observability and incident resolution

Monte Carlo Data provides end-to-end data observability and AI-powered data reliability for real-time monitoring of data quality, freshness, volume, and schema to help data engineers ensure data integrity. Its platform continuously observes data pipelines and datasets, surfaces issues through alerts and dashboards, and includes incident detection and resolution that trigger workflows in tools like Slack, Teams, and JIRA. The service differentiates itself by focusing specifically on data reliability and end-to-end observability with built-in incident management and cross-tool integrations, rather than generic monitoring. The goal is to prevent bad data from affecting business decisions by enabling reliable, transparent, and scalable data operations that teams can trust.

Company size
501-1,000
Company stage
Series D
Total funding
$236M
Headquarters
San Francisco, CA
Founded
2019

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