Enterprise Account Executive

About the role

This position drives seven-figure platform deployments for global enterprises where accuracy is essential. Owners of these deals establish the standard for AI-powered automation across large-scale, complex customer environments.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Complex workflows are handled by deploying the platform to ensure accuracy is non-negotiable for global enterprises.

Solutions Engineering partners to validate enterprise architecture and support technical validation for platform deployments.

Value is communicated to C-suite and board-level audiences to demonstrate enterprise credibility and platform impact.

C-suite executives and senior enterprise decision-makers develop relationships to influence strategic initiatives.

Quotas are met by pursuing enterprise deals ranging from $250K to $2M+ ACV across 9-18 month sales cycles.

Requirements

A degree is required for this position.

5+ years selling enterprise software with consistent $1M+ annual quota achievement is mandatory.

Experience closing deals $250K+ ACV in Fortune 1000 environments is mandatory.

Experience with complex enterprise sales cycles of 6+ months, procurement processes, and security/compliance requirements is mandatory.

Enterprise IT and business stakeholder relationships must be established and maintained.

Matrix organizations are navigated by building consensus among multiple decision-makers.

Account growth is achieved through expanding existing accounts and strategic relationship building.

Nice to have

Experience selling AI/ML platforms, automation tools, or enterprise SaaS is preferred.

Background with document-heavy industries such as Finance, Healthcare, or Supply Chain is preferred.

History at high-growth enterprise software companies with $100M+ ARR is preferred.

Technical aptitude to understand platform capabilities and enterprise integration requirements is preferred.

Practical notes

Hybrid work requires twice-a-week presence in the Palo Alto office for team collaboration.

The role is based in the Bay Area, CA, with compensation influenced by location, experience, and internal equity.

for application and hiring process information.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Roles of this type focus on enterprise software sales cycles and complex procurement.

Platforms in this field often integrate with major enterprise systems like SAP and Salesforce.

General sales tools support relationship management and quota achievement.

Industry knowledge in document-heavy sectors helps position platform value.

High-growth environments typically demand consistent quota attainment and adaptability.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.