Strategic Account Executive
About the job
- Consistently win larger enterprise deals with new logos through complex, multi-year enterprise sales cycles and meet or exceed quota by building a pipeline rather than expanding an inherited book.
- Have sales experience in the data ecosystem, observability, or infrastructure, and be able to discuss data pipelines, observability, and AI readiness with technical buyers.
- Use value-based selling to identify customer pain, develop internal champions, build business cases, and hold business conversations with C-level buyers about risk, reliability, and outcomes.
- Use MEDDPICC to qualify deals.
- Have experience succeeding in startup or scale-up environments without an established playbook, creating urgency around problems without an existing budget line, and responding quickly to coaching.
- Be comfortable working deeply with technical buyers alongside business stakeholders and managing 3–6 month, multi-stakeholder enterprise deals.
- Prioritize a 50–60-account territory and build a self-sustaining pipeline with an SDR through outbound, account-based marketing, partner co-selling, and AI tools, maintaining 3–4x quota coverage.
- Reach economic buyers, including CTOs, CDOs, and VPs of Data or AI, early; develop internal champions and engage multiple stakeholders in every deal.
- Lead discovery, co-build Business Value Assessments with Sales Engineering, and present the business case to executive decision-makers.
- Close six- and seven-figure, multi-year deals through procurement, security review, and legal processes.
- Write account plans that expand an initial customer win into a multi-department footprint.
- Share competitive intelligence and deal learnings with Sales Leadership, Product, and Marketing to improve the sales playbook.
- Experience with usage-based pricing, or a genuine interest in forecasting when deal size changes with adoption.
About the company
Monte Carlo Data provides end-to-end data observability to improve data reliability. Its platform continuously monitors real-time data status, including freshness, volume, schema, and quality, so data engineers can verify data integrity. It includes incident detection and resolution tools that alert, investigate, and prevent data issues at scale, and it integrates with Slack, Teams, and JIRA to fit existing workflows. The company aims to help data-dependent organizations avoid bad data by enabling reliable, transparent, and scalable data operations.
Company Size
501-1,000
Company Stage
Series D
Total Funding
$236M
Headquarters
San Francisco, California
Founded
2019
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Simplify's Take
What believers are saying
- Monte Carlo says 73% of enterprises require agent monitoring before deployment.
- Axios uses Monte Carlo for AI content tagging accuracy, with 12 more LLM apps planned.
- The company launched Machine Learning Metric Monitors in 2026, broadening beyond data reliability.
What critics are saying
- Monte Carlo cut about 30% of staff in March 2026, signaling pressure on growth.
- Snowflake, Databricks, and open-source stacks bundle observability, compressing Monte Carlo pricing by 2027.
- Agent observability commoditizes, and Monte Carlo becomes a feature inside cloud platforms.
What makes Monte Carlo Data unique
- Monte Carlo unifies data and AI observability across context, performance, behavior, and outputs.
- Its March 12, 2026 Agent Observability spans predeployment evals and production tracing.
- Databricks Agent Bricks integration on June 15, 2026 deepens enterprise platform lock-in.
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Benefits
- Remote Work Options