Sales Representative

Overview

You're joining as the first sales hire at Meaningful Data, a founder-led startup selling AI-powered master data management and predictive analytics to enterprises. You'll be doing full-cycle sales—prospecting, demoing, closing, and likely handling post-sale relationships. The founder is technical (CEO/Founder with IIT background), so you'll get product support but limited sales mentorship. You're building the go-to-market motion from scratch.

Role Snapshot

Company Context

Stage: Pre-seed/Bootstrap (1 employee listed, founder-led)

Size: 1-2 employees

Growth: Very early - you'd be employee #2 or #3

Market Position: Unknown brand entering a crowded master data management space (competing against Informatica, Profisee, Reltio, Semarchy)

GTM Reality

Pipeline Sources:

• 100% Outbound - No marketing engine, no inbound leads, no brand recognition yet
• You'll build target account lists from scratch
• Cold outreach via LinkedIn, email, possibly calling into IT/data orgs
• May leverage founder's network initially, but that dries up fast

SDR/AE Structure: You ARE the SDR and AE - self-sourcing everything

SE Support: Founder may do technical demos early on, but you'll need to learn the product deeply

Competitive Landscape

Main Competitors: Informatica MDM, Profisee, Reltio, Semarchy, plus internal "build it ourselves" mentality

How They Differentiate: Combines master data management with LLMs and predictive analytics - but unclear how mature the product is

Common Objections:

• "Who are you?" (brand unknown)
• "We already use [Informatica/other]"
• "Our data team can build this"
• "Too risky to bet on an unproven vendor"
• Procurement will flag company size/stability concerns

Win Themes: AI-native approach, potentially more flexible/faster than legacy tools, founder-involved partnership

What You'll Actually Do

Time Breakdown

Prospecting (40%) | Active Deals (30%) | Product Learning (15%) | Internal Ops (15%)

Key Activities

• Building Target Lists: Research companies with data quality/integration problems. Look for data transformation projects, AI initiatives, or companies with messy M&A history. Start with mid-market enterprises (500-2000 employees) where you can actually reach decision-makers.
• Cold Outreach: LinkedIn messages, cold emails to VP Data, Chief Data Officers, Head of Analytics. Most won't respond. You're unknown. Expect 1-2% response rates initially.
• Discovery Calls: When you do get meetings, you're diagnosing their data architecture, understanding where their master data breaks, identifying pain points around data quality and AI readiness. These buyers are technical and skeptical.
• Product Demos: Either you deliver them or coordinate with the founder. These are complex—you're showing how LLMs work with their data models. Expect lots of technical questions you can't answer at first.
• Building Sales Process: Creating your own email templates, demo decks, qualification frameworks. Nothing exists. You're documenting as you go.
• Internal Meetings: Weekly syncs with founder to discuss pipeline, product gaps, pricing questions. You'll influence product roadmap whether you want to or not.

The Honest Reality

What's Hard

• No Brand Recognition: You're selling an unknown product in a space dominated by established players. Most prospects have never heard of you. Doors are heavy.
• Long, Complex Sales: Data infrastructure deals involve IT, data teams, security, compliance, procurement. Lots of stakeholders, lots of delays. Most of your pipeline will push quarters.
• Building From Zero: No CRM hygiene, no email sequences, no battle cards, no case studies, no proven messaging. You create everything.
• Product Uncertainty: Unclear how mature the product is. Expect gaps. Expect prospects to ask for features that don't exist. You'll hear "can you do X?" and have to check with the founder constantly.
• Founder Dependency: Technical demos, custom solutions, pricing decisions—everything runs through a technical founder who's also CEO, which means bottlenecks.
• Isolation: No sales team to learn from. No manager giving you coaching. You figure it out or you don't.

What Success Looks Like

• You close 2-3 enterprise deals in year one ($300K-500K in bookings)
• You build a repeatable outbound motion that generates 5-10 qualified conversations per month
• You document what works so the company can hire sales rep #2
• You become a trusted product advisor—founder values your market feedback

Who You're Selling To

Primary Buyers:

• VP/Director of Data Engineering or Data Governance (technical buyer)
• Chief Data Officer or Chief Analytics Officer (economic buyer at larger orgs)
• VP IT or CTO (often involved in vendor selection)

What They Care About:

• Data quality and consistency across systems (ERP, CRM, analytics platforms)
• AI readiness—can they trust their data for ML models?
• Integration complexity—how hard is implementation?
• Vendor risk—are you going to be around in 2 years?
• ROI timeline—data projects are notorious for dragging on

Requirements

• 3-5+ years selling B2B software, ideally data/analytics/infrastructure tools
• Comfortable with technical buyers—you need to speak enough data architecture to be credible
• Self-starter mentality—no one is managing you day-to-day
• Enterprise sales experience with 6+ month cycles preferred
• Willing to take startup risk (equity over cash, uncertainty, no safety net)
• Comfortable with ambiguity and building processes from scratch