Account Executive

Overview

You sell DoiT's Cloud Intelligence platform to mid-market and enterprise companies running significant cloud infrastructure. Your buyers are VPs of Engineering, FinOps Directors, and CFOs at companies spending six to seven figures annually on AWS, Google Cloud, or Azure. You're selling against native cloud tools (AWS Cost Explorer, GCP's cost management), other FinOps platforms, and the status quo of manual spreadsheet tracking.

Role Snapshot

Company Context

Stage: Growth stage (678 employees suggests late-stage or profitable)

Size: 678 employees

Growth: Actively hiring across sales org (multiple AE, SDR, manager roles)

Market Position: Established player in FinOps/cloud optimization competing against native tools, Cloudability, Apptio/CloudHealth, Vantage, and newer AI-driven platforms

GTM Reality

Pipeline Sources:

• 40% Inbound - companies searching for cloud cost optimization, responding to content/webinars, or outgrowing spreadsheets
• 50% Outbound - targeting companies with visible cloud spend (job postings for cloud engineers, tech stack signals, funding announcements)
• 10% Partners/Referrals - cloud consultancies and reseller partnerships

SDR/AE Structure: Dedicated SDR team feeds qualified meetings; AEs also expected to self-source 30-40% of pipeline

SE Support: Shared solutions engineer pool for demos and technical validation

Competitive Landscape

Main Competitors:

• Native cloud tools (AWS Cost Explorer, GCP Cost Management, Azure Cost Management)
• CloudHealth/Apptio, Cloudability, Vantage, Finout
• Internal "we built our own" solutions

How They Differentiate: Multi-cloud support, "intent-aware" AI optimization that goes beyond cost to include reliability/performance, claim of 10x higher implementation rate vs competitors

Common Objections:

• "We already have AWS Cost Explorer/native tools"
• "Our engineering team built custom dashboards"
• "We're not ready to add another vendor/platform"
• "Prove ROI before we commit to annual contract"

Win Themes: Multi-cloud complexity, automated remediation vs just recommendations, time savings for engineering teams, granular cost allocation across teams

What You'll Actually Do

Time Breakdown

Prospecting (25%) | Active Deals (50%) | Internal (25%)

Key Activities

• Discovery calls with FinOps/Engineering leaders: You're asking about their current cloud spend, how they track it today, pain points with showback/chargeback, and whether they have headcount to manually implement optimization recommendations. Most prospects have tried native tools and found them insufficient but need convincing a platform is worth the cost.

• Multi-threading into technical and financial buyers: You need both the VP Engineering (cares about reliability/performance) and the CFO/finance team (cares about cost savings). Getting both aligned on timeline and budget is where deals stall most often.

• Coordinating technical evaluations: You're scheduling SE-led demos of the platform, arranging POCs where DoiT analyzes their actual cloud bill, and proving ROI with custom savings projections. These can take 4-8 weeks and prospects often go dark mid-evaluation.

• Navigating procurement and security reviews: Once you have technical buy-in, you're chasing legal, infosec, and procurement through vendor questionnaires, SOC 2 audits, and contract redlines. Many deals slip quarters here.

The Honest Reality

What's Hard

• Proving ROI against free tools: Every cloud provider offers basic cost management for free. You're selling a premium platform and constantly defending why it's worth the incremental spend when engineering teams are skeptical of vendor lock-in.

• Long, multi-stakeholder cycles: You need engineering, finance, security, and procurement all aligned. One stakeholder going on vacation or shifting priorities can push your deal 4-6 weeks. Most of your pipeline will slip at least once.

• Technical complexity and integration concerns: Prospects worry about granting cloud account access, integration lift, and whether their engineering team will actually use another tool. You're selling behavior change as much as software.

What Success Looks Like

• Closing 8-12 deals per year in the $50-250K range
• Building pipeline 3-4x your quarterly quota (most deals take 2+ quarters)
• Converting 20-25% of qualified opportunities to closed-won
• Expanding into existing accounts as cloud spend grows (most revenue comes from account expansion over time)

Who You're Selling To

Primary Buyers:

• VP/Director of Engineering or Infrastructure (technical champion)
• FinOps Director/Manager or CFO (economic buyer)
• IT/DevOps Directors managing cloud operations

What They Care About:

• Measurable cost savings that justify platform cost (usually need 3-5x ROI)
• Time savings for engineering team vs manual optimization work
• Multi-cloud visibility if they run AWS + GCP or AWS + Azure
• Automated implementation of recommendations (not just alerts)
• Granular cost allocation and showback across teams/products
• Not adding more toil or requiring engineering resources to maintain

Requirements

• 3-5 years selling technical infrastructure or SaaS to engineering/IT buyers
• Experience with consultative enterprise sales (3-6 month cycles, $50K+ deals)
• Ability to speak credibly about cloud infrastructure (AWS, GCP, Azure) without being a practitioner
• Comfort multi-threading across technical and financial stakeholders
• Track record managing 15-25 active opportunities simultaneously
• Self-sourcing skills - you'll need to generate 30-40% of your own pipeline through outbound
• Resilience through long sales cycles and deals that stall/slip frequently