SDR (Sales Development Representative)

Overview

You're cold-calling and emailing data engineers, analytics engineers, and heads of data/platform at companies that are juggling Fivetran + Monte Carlo + Atlan + Census (or similar tool sprawl). Your job is to get them interested enough in consolidating their data stack to take a 30-minute demo with an AE. This is technical outbound—you need to understand ETL, reverse ETL, data observability, and catalogs well enough to have credible conversations.

Role Snapshot

Company Context

Stage: Early stage (41 employees, likely Seed/Series A based on size)

Size: 41 employees

Growth: Actively hiring across GTM roles (2 SDRs, DevRel, Head of Partnerships)

Market Position: Category consolidator in a crowded space—competing against multiple point solutions (Fivetran, Airbyte, Monte Carlo, Atlan, Census, Hightouch)

GTM Reality

Pipeline Sources:

• 90%+ Outbound - You're building lists, sequencing prospects, and cold calling
• ~10% Inbound - Some website conversions and community interest, but don't expect a full pipeline handed to you
• Partners/Referrals - Likely minimal at this stage

SDR/AE Structure: You're booking for AEs. Clear handoff structure. Internal promotion path to AE is real and emphasized.

SE Support: Unknown, but given technical product, likely shared SE support for demos.

Competitive Landscape

Main Competitors:

• Point solutions: Fivetran/Airbyte (ETL), Monte Carlo/Metaplane (observability), Atlan/Collibra (catalog), Census/Hightouch (reverse ETL)
• All-in-one plays: Databricks, Snowflake ecosystem plays

How They Differentiate: "Why pay for 4 tools when you can have one unified platform?" Cost savings + single source of truth pitch.

Common Objections:

• "We already have Fivetran and it works fine"
• "We've invested heavily in our current stack"
• "Switching costs are too high"
• "How mature is your product vs. established players?"

Win Themes: TCO reduction, unified observability, faster time-to-insight, less tool sprawl for data teams to manage.

What You'll Actually Do

Time Breakdown

Prospecting/Outreach (60%) | Follow-ups/Nurture (25%) | Internal/Admin (15%)

Key Activities

• Cold Calling: 50-60 calls per day to data engineers and analytics leads. Most go to voicemail. You're trying to catch people who are frustrated with their current tool stack. Expect a lot of "we're happy with what we have" responses.
• Email Sequencing: Multi-touch sequences explaining the consolidation value prop. You need to write technical emails that don't sound like typical SaaS spam—these buyers can smell BS.
• LinkedIn Outreach: Connecting with data team members, commenting on relevant posts about data observability or pipeline issues, building a presence in data engineering circles.
• Qualifying Conversations: When someone bites, you're asking about their current stack, pain points with tool sprawl, team size, and whether they have budget/authority to evaluate new tools. You need to know enough to separate tire-kickers from real opportunities.
• Demo Handoffs: Prepping AEs on the account context before they take the meeting. If you pass junk, you'll hear about it.

The Honest Reality

What's Hard

• Technical Learning Curve: You need to understand data infrastructure concepts quickly. Data engineers will grill you on technical details and can tell if you're faking it.
• Low Response Rates: Data engineers are bombarded by vendors. Getting replies is tough. You'll send 100 emails to get 3-5 responses.
• Long Sales Cycles (for AEs): Even though you just book the meeting, knowing deals take 3-6 months means you won't see immediate wins. You're planting seeds.
• Early-Stage Chaos: You're at 41 people. Messaging will change. ICP will shift. What worked last month might not work this month. High ambiguity.
• Nights and Weekends: The poster was explicit—this isn't 9-5. Early stage means high urgency and long days.

What Success Looks Like

• Consistently booking 15-20 qualified meetings per month
• 30%+ show rate on booked meetings
• 50%+ of your meetings convert to next steps with AE
• Building pipeline that converts—AEs actually close deals from your meetings
• Getting promoted to AE within 12-18 months (they promote internally)

Who You're Selling To

Primary Buyers:

• Data Engineers (IC level)
• Analytics Engineers
• Heads of Data/Analytics
• VPs of Data Platform
• CTOs at smaller companies

What They Care About:

• Tool Consolidation: They're tired of managing 4+ data tools with overlapping functionality
• Cost: "Are we overpaying for our current stack?" is a real question at budget renewal time
• Reliability: Data pipelines break. They want better observability and faster debugging
• Vendor Risk: Can they trust a 41-person startup with mission-critical data infrastructure?

Requirements

• Comfortable with high-volume outbound (50+ daily activities)
• Ability to learn technical concepts quickly—you'll be talking about ETL, data pipelines, observability, and metadata management
• Coachable and hungry—they want people who will put in the work and take feedback
• Resilience to rejection—most calls/emails go nowhere
• Genuine interest in career progression to AE (they explicitly offer this path)
• Okay with early-stage intensity—long days, high urgency, things change fast