Account Executive

Overview

You sell Datalogz to data teams drowning in BI tool sprawl. Your buyers are analytics leaders at mid-market to enterprise companies who have 3+ BI tools (Tableau, Power BI, Looker, etc.) and no visibility into who's using what, what it's costing them, or where security risks hide. You run full-cycle deals from demo to close.

Role Snapshot

Company Context

Stage: Early-stage (31 employees, likely Seed/Series A funded or bootstrapped)

Size: 31 employees

Growth: Small enough that everyone wears multiple hats. You'll likely be one of the first AEs helping build the playbook.

Market Position: Category creator in BI governance - they're educating the market that this problem exists and needs solving. Not competing against direct alternatives so much as against "doing nothing" or internal tools.

GTM Reality

Pipeline Sources:

• 60-70% Outbound - You're identifying companies with messy BI environments (multiple tools, 1000+ employees, data-driven culture) and cold outreaching
• 20-30% Inbound - Some leads from content marketing to data/analytics leaders, but not huge volume at this stage
• 10% Referrals/Network - Existing customers referring you to similar teams

SDR/AE Structure: At 31 people total, likely no dedicated SDR team or shared pool at best. You're probably self-sourcing 50%+ of your pipeline.

SE Support: Founder or technical co-founder likely does early technical demos. May have one technical person helping, but you're on your own for most discovery and initial demos.

Competitive Landscape

Main Competitors:

• Internal DIY solutions (data teams building their own metadata tracking)
• Broader data catalog tools (Alation, Collibra) that touch BI metadata but aren't purpose-built for it
• Status quo / spreadsheets

How They Differentiate: Purpose-built for BI tool governance specifically. Integrates across the whole BI stack (Tableau, Power BI, Looker, etc.) rather than being single-tool focused.

Common Objections:

• "We already have a data catalog"
• "Our BI admins can track this manually"
• "This isn't a priority right now" (especially when budgets tighten)
• "What's the ROI?" (you need to prove cost savings from consolidation/optimization)

Win Themes:

• Concrete cost savings from identifying unused licenses and duplicate content
• Risk mitigation (finding stale dashboards with bad data, security vulnerabilities)
• Time savings for BI admins currently doing this work manually

What You'll Actually Do

Time Breakdown

Prospecting (40%) | Active Deals (35%) | Internal/Admin (25%)

Key Activities

• Cold Outbound: You build lists of companies with complex BI environments (Fortune 2000, high Glassdoor mentions of "data-driven", job postings for BI engineers). Send sequences targeting VP/Director of Analytics, Data Engineering, or BI/Analytics leads. Maybe 20-30 new touches per day.

• Discovery Calls: You talk to analytics leaders about their BI tool footprint. How many tools? How many users? What's the governance process? Where are the pain points? You're diagnosing if they have a problem worth solving. These calls reveal whether they have budget authority or if you need to go higher.

• Product Demos: You show how Datalogz gives visibility into their BI mess - who's using what, which dashboards are stale, where costs are hidden, security risks. Often you're screen-sharing into their actual BI environment to prove the value. Demos take 45-60 minutes because you're educating on a problem they may not have fully articulated.

• Multi-threading: Deals require buy-in from analytics leaders, finance (for cost savings), and sometimes security/compliance. You're setting up meetings with 3-4 stakeholders, sending ROI calculations, building business cases. A lot of your time is chasing people for the next meeting.

• Negotiating Procurement: Once they're sold, you deal with procurement, legal reviews of your MSA, security questionnaires. This can drag 4-6 weeks at enterprise companies.

• Internal: Weekly forecast calls, CRM hygiene, deal reviews with the founder/CEO. At a 31-person company, you're probably in company all-hands and giving product feedback directly to engineering.

The Honest Reality

What's Hard

• Market Education: You're not selling against competitors, you're selling against "we didn't know this was a problem." That means longer sales cycles and more deals dying in status quo.

• Budget Competition: This isn't core BI infrastructure, it's governance tooling. When budgets get cut, "nice to have" governance tools get deprioritized. You'll lose deals to hiring freezes or reprioritization.

• Economic Buyer Complexity: The analytics leader feels the pain but CFO controls budget. You need to prove ROI in dollars, which means digging into their BI spending and building detailed cost models. Some deals require 3-4 stakeholders to align.

• Small Company = Building Playbook: At 31 people, there's no mature sales process. You're figuring out messaging, ideal customer profile, and deal stages as you go. That's exciting if you like autonomy, frustrating if you want a proven playbook.

What Success Looks Like

• Pipeline Generation: You build 3-4x your quota in pipeline because not everything closes. That's 10-12 qualified opportunities per quarter.

• Deal Velocity: You close 1-2 deals per quarter at $30-75K ACV. Most deals take 4-6 months, so you're constantly balancing early-stage prospecting with late-stage closing.

• Customer Expansion: Early customers become case studies and references. You leverage them to open doors with similar companies.

Who You're Selling To

Primary Buyers:

• VP/Director of Analytics or Data
• Head of Business Intelligence
• Director of Data Engineering (sometimes)

What They Care About:

• Cost: Can you prove we're wasting $X on unused BI licenses or redundant tools?
• Risk: Are we exposing bad data in executive dashboards? Do we have security vulnerabilities in our BI tools?
• Efficiency: How much time are our BI admins spending on manual governance tasks?
• Scale: Can we enable self-service analytics without losing control?

Requirements

• 3-5 years selling B2B software, ideally into data/analytics/engineering buyers
• Experience with technical sales (understanding BI tools like Tableau, Power BI, Looker helps)
• Comfortable with consultative, ROI-driven selling (you're building business cases, not demoing features)
• Self-starter mentality - at 31 people, there's limited structure and you'll be building your own process
• OK with ambiguity and early-stage chaos - playbooks are evolving, product is maturing, you're figuring it out together
• Bonus: Experience selling data infrastructure, governance, or analytics tooling