Senior Manager of Revenue Operations

Overview

You're building the revenue operations foundation for Materialize, a VC-backed data infrastructure company selling to enterprise technical buyers. You'll own Salesforce configuration, sales process design, pipeline analytics, and forecasting—essentially being the operational backbone between the sales team and leadership. You'll spend your time in Salesforce admin, building dashboards, defining what gets tracked, and troubleshooting why the forecast is off.

Role Snapshot

Company Context

Stage: Series C+ (backed by Kleiner Perkins, Redpoint, Lightspeed)

Size: Likely 100-300 employees (scaling infrastructure startup)

Growth: Hiring for multiple senior RevOps roles signals GTM expansion

Market Position: Category creator in AI-native data infrastructure - educating market on new approach to streaming databases for real-time AI applications

GTM Reality

Pipeline Sources:

• 70-80% Outbound - AEs and SDRs targeting data engineers, platform teams, AI/ML engineers at enterprises
• 15-25% Inbound - technical content, community, open source users converting to enterprise
• 5-10% Referrals/network in data infrastructure community

SDR/AE Structure: Likely dedicated SDRs supporting AEs, possibly sales engineers for technical demos

SE Support: Almost certainly dedicated SEs given technical product complexity

Competitive Landscape

Main Competitors: Traditional data warehouses (Snowflake, Databricks), streaming platforms (Confluent/Kafka), real-time databases

How They Differentiate: Purpose-built for AI-native use cases, faster real-time processing for ML models

Common Objections: "We already have Snowflake", "Can't we build this ourselves?", "Too early/unproven for our use case"

Win Themes: Performance for real-time AI, reduces complexity vs DIY, forward-looking architecture

What You'll Actually Do

Time Breakdown

Salesforce Admin (30%) | Analytics/Reporting (25%) | Process Design (20%) | Stakeholder Meetings (15%) | Hiring/Team Building (10%)

Key Activities

• Salesforce Architecture: Building custom objects, fields, workflows, and automations for complex enterprise sales. Lots of time fixing data quality issues, dealing with "why aren't opportunities syncing" questions, and building validation rules that get overridden anyway.
• Forecasting & Pipeline Reviews: Building the weekly forecast model, preparing pipeline deep-dive decks for leadership, investigating why deals slipped, and getting yelled at when the forecast is off even though sales didn't update Salesforce.
• Process Definition: Writing the sales methodology playbook, defining stage exit criteria, building qualification frameworks (MEDDIC/MEDDICC likely), then watching sales ignore it until leadership enforces it.
• Analytics & Dashboards: Building Tableau/Looker dashboards on rep productivity, conversion rates, velocity metrics. Spending lots of time explaining why the numbers look different than what sales thinks they should be.
• Tool Stack Management: Owning integrations between Salesforce, Outreach/Salesloft, Gong, LinkedIn Sales Navigator, enrichment tools. Fielding "this integration broke" Slack messages constantly.
• Cross-functional Projects: Working with Marketing on lead routing and attribution, Finance on revenue recognition, Product on usage data integration, Customer Success on expansion tracking.

The Honest Reality

What's Hard

• Garbage In, Garbage Out: Sales reps don't update Salesforce religiously. You'll spend enormous energy on data hygiene and still have dirty data. Building beautiful reports on bad data is frustrating.
• Everyone's Ops Therapist: You're the person who gets pinged for everything from "how do I build a report" to "why did this lead get assigned to the wrong rep" to "can you pull this one-off analysis by EOD."
• Caught in the Middle: Leadership wants perfect forecasts and insights. Sales wants you to build things that make their lives easier. These are often in conflict. You'll get squeezed.
• Changing Priorities: At a scaling startup, GTM strategy shifts. You'll build processes that get scrapped, implement tools that don't get adopted, and redo territory planning three times in a year.
• Technical Learning Curve: You need to understand what Materialize actually does well enough to design intelligent sales processes. This is complex infrastructure software, not simple SaaS.

What Success Looks Like

• Forecast accuracy within 10% consistently
• Sales reps actually use the processes and systems you build
• Leadership makes decisions based on your dashboards and insights
• Clean pipeline hygiene - deals are qualified properly, stages reflect reality
• You've built leverage - things run without you constantly firefighting

Who You're Supporting

Internal Stakeholders:

• VP Sales / CRO - your main customer, wants forecast accuracy and pipeline visibility
• AEs and SDRs - want systems that make their jobs easier, not harder
• Finance - needs clean data for revenue recognition and board reporting
• Marketing - wants closed-loop attribution and lead quality feedback

What They Care About:

• Leadership: Predictable revenue, data-driven decisions, scalable processes
• Sales Team: Less admin work, better territory/comp plans, tools that actually help
• Finance: Clean data, revenue recognition accuracy, no surprises

Requirements

• 5-7+ years in Revenue Operations, Sales Operations, or similar analytical sales role
• Deep Salesforce expertise - admin certification highly preferred, you need to know objects/workflows/validation rules cold
• Experience at B2B SaaS/infrastructure companies, ideally technical products
• Strong in Excel/Google Sheets and BI tools (Tableau, Looker, Mode)
• Background supporting $10M+ ARR sales organizations
• Comfortable in ambiguity - this is a scaling startup, not a defined playbook
• Ideally: experience in technical/developer-focused GTM motions
• Bonus: Python/SQL skills for deeper data analysis
• Must be in NYC (in-office role)