Head of Revenue Systems

Overview

You run revenue systems for a Series D data infrastructure company selling to technical buyers. Redpanda sells streaming data platform technology (Kafka alternative) to enterprises building real-time applications and AI systems. You're managing the tech stack and data architecture that supports both a product-led growth motion (self-serve users upgrading) and a traditional enterprise sales team going after six-figure deals. Your systems need to handle usage data from the product, connect it to CRM, and give reps actionable intelligence on when to reach out.

Role Snapshot

Company Context

Stage: Series D (raised $165M total, $1B valuation as of April 2025)

Size: 196 employees

Growth: Raised $100M Series D recently, expanding GTM motion to capitalize on AI/real-time data infrastructure demand

Market Position: Challenger to Confluent (market leader) and AWS MSK. They compete on performance (6x lower costs, sub-second latency claimed) and developer experience. Playing in a technical category where buyers are data engineers, platform architects, and infrastructure teams.

Competitive Landscape: Going against well-funded incumbents (Confluent is public, AWS has infinite resources). Redpanda's angle is simpler deployment, better performance, lower cost. They also have strong G2 ratings (4.8 stars) suggesting good product-market fit.

GTM Reality

Dual Motion Complexity:

• PLG Path: Developers can try Redpanda via self-serve (likely cloud version). Some portion convert to paid. Your systems need to track product usage signals and trigger sales outreach when accounts show buying intent.
• Enterprise Path: Traditional outbound to larger accounts with 6-12+ month sales cycles. These deals involve multiple stakeholders (infra leads, engineering directors, sometimes CTO/VP Eng).

What This Means for You: You're stitching together product telemetry, CRM data, and sales workflows. The PLG motion generates signals ("this team just spun up 5 clusters"), and you need systems that route those to the right rep with context. The enterprise motion needs traditional pipeline management, forecasting, and account planning tools.

Current State Guess: At 196 people with recent funding, they're probably scaling fast. Revenue systems are likely a mix of what worked early-stage and net-new infrastructure needs. You're inheriting technical debt and being asked to professionalize it while supporting 2x growth targets.

SDR/AE Structure: Likely a mix — SDRs for outbound, AEs handling both inbound PLG leads and their own prospecting for enterprise

Systems Maturity: Mid-stage. Salesforce is there, but probably needs architecture work. Integrations exist but may be fragile. Reporting is functional but not elegant.

Competitive Landscape

Main Competitors:

• Confluent (the Kafka leader, public company, huge budget)
• AWS MSK (Managed Streaming for Kafka)
• Apache Kafka (open source, self-managed)

How Redpanda Differentiates:

• No JVM/Zookeeper dependencies (simpler ops)
• Claims 6x cost savings and better performance
• Developer experience and ease of deployment

What This Means for Systems: Deals take education and proof. Buyers need to see data (benchmarks, cost analysis). Your systems should support how reps build business cases and track technical validation/POCs. Competitive intel needs to be accessible in CRM.

What You'll Actually Do

Time Breakdown

Salesforce/CRM Architecture (30%) | Cross-functional Projects (25%) | 
Data/Analytics (20%) | Vendor Management (15%) | Firefighting (10%)

Key Activities

• Salesforce Administration & Architecture: You're rebuilding object models, fixing automation that breaks, and managing permissions. At this stage, the CRM probably wasn't built for dual-motion GTM. You're refactoring how opportunities work for PLG leads vs enterprise deals, making sure stages and fields make sense for both. Expect to spend time in Process Builder, Flows, and Apex when things get complex.

• Pipeline Signal & Routing: You build the integrations that pull product usage data into Salesforce. When a self-serve user's trial cluster hits certain thresholds, your system creates a lead, routes it to the right AE, and surfaces context (what features they're using, how much data they're moving). This involves working with product/data teams, building reverse ETL pipelines, and setting scoring logic.

• Reporting & Forecasting Infrastructure: Sales leadership wants pipeline visibility, conversion metrics, and forecast accuracy. You own the reports, dashboards, and underlying data quality. You're constantly cleaning data (reps don't update stages, deals sit in "Negotiation" for 4 months) and building automated checks. You also support board-level reporting — ARR, net retention, pipeline coverage metrics.

• Tool Selection & Integration: You evaluate and implement new tools. Maybe the team needs conversation intelligence (Gong/Chorus), better outbound sequencing (Outreach/Salesloft), or product analytics (Pendo/Amplitude) integrated with CRM. You run RFPs, negotiate contracts, and own the implementation. Then you maintain integrations when APIs change or vendors deprecate features.

• Process Design & Enablement: Sales wants a new process (account planning, QBRs, partner deal registration). You figure out how to systemize it. You map workflows, configure the tools, document the process, and train people. Then you monitor adoption and iterate when it doesn't work how they expected.

• Revenue Operations Governance: You set data standards, manage user provisioning, audit security/permissions, and enforce hygiene. You're the one who says "no, we're not adding another custom field" and explains why duplicate leads break everything.

The Honest Reality

What's Hard

• Dual-Motion Complexity: PLG and enterprise sales have fundamentally different rhythms. PLG users want self-serve, fast movement, product-led everything. Enterprise deals need human touch, customization, and executive involvement. Building systems that serve both without creating Frankenstein workflows is hard. You'll spend a lot of time in meetings explaining why "just add a checkbox" doesn't work.

• Technical Product, Technical Buyers: Redpanda sells to data engineers and infrastructure teams. These buyers care about latency benchmarks, throughput specs, and deployment architecture. Your systems need to track technical evaluations (POCs), benchmark results, and architectural discussions. This isn't transactional — deals involve GitHub repos, Slack channels with prospects, and weeks of technical back-and-forth. CRM wasn't built for this.

• Scale Growing Pains: They just raised $100M at a $1B valuation. That means growth targets are aggressive. Your systems are probably behind where they need to be. You'll inherit technical debt (bad data, brittle integrations, manual workarounds) and be asked to fix it while supporting 2x headcount growth in sales. Priorities shift fast.

• Cross-Functional Dependency Hell: You need product to instrument telemetry. You need engineering to expose APIs. You need finance for billing system integration. You need marketing for lead scoring logic. Everyone's busy with their own roadmap. You're constantly negotiating for resources and dealing with "we'll get to it next quarter."

What Success Looks Like

• Reps Use Your Systems: The PQL (Product-Qualified Lead) scoring you built actually routes hot leads to reps, and they close them. AEs check the product usage dashboard you built before every call. That's the win — systems that provide signal, not noise.

• Pipeline Visibility That's Trusted: Sales leadership stops asking you to "pull the real numbers" because the dashboard is accurate. Forecast calls reference your reports. The board deck uses your ARR waterfall. That means clean data and reliable infrastructure.

• Scalable Infrastructure: You can onboard 10 new AEs without manually provisioning 15 tools each. Automation works. Integrations don't break every week. You're spending time on strategic projects, not firefighting.

Who You're Selling To (Your Internal Stakeholders)

You're Serving:

• CRO / VP Sales: Wants pipeline visibility, forecast accuracy, and systems that help reps close faster
• AEs / SDRs: Want clean data, fast workflows, and signals that tell them who to call
• Finance: Needs revenue recognition to be accurate and auditable
• Product/Engineering: You're the bridge — you translate their usage data into sales context

What They Care About:

• Sales Reps: "Does this save me time or make me do more admin?"
• Leadership: "Can I trust this forecast? What's our pipeline coverage? Why is our win rate dropping?"
• Finance: "Does this integrate with NetSuite? Can we pass SOC2 audit?"
• Product: "What signals should we track? How do we know if PLG is working?"

Requirements

• Salesforce Expertise: You've built complex Salesforce orgs from scratch or refactored messy ones. You know data modeling, automation (Flow, Process Builder, Apex), and integration patterns. You've dealt with multi-motion GTM (PLG + enterprise, or similar complexity).

• Technical Fluency: You can talk to engineers about APIs, webhooks, and data pipelines. You understand SQL and can write queries to audit data. You've worked with reverse ETL tools (Census, Hightouch) or built similar integrations. You know how product analytics tools work and how to pull usage data into CRM.

• End-to-End Systems Thinking: You've owned lead-to-cash processes. You understand how marketing automation, CRM, billing, and customer success platforms connect. You've implemented or optimized CPQ, revenue recognition, or subscription management tools.

• Stakeholder Management: You can translate technical constraints into business language. You know how to prioritize when everyone says their project is urgent. You've managed vendors, negotiated contracts, and run RFP processes.

• Data Infrastructure Background Preferred: Experience with B2B SaaS selling to technical buyers (data, dev tools, infrastructure) is a big plus. You understand how engineers evaluate products and what "product-led" actually means in a technical category.

• Startup Scale Experience: You've been through hypergrowth (Series B → D or similar). You've built systems for 50-200 person sales orgs. You know what breaks at scale and how to fix it without rebuilding everything.