RevOps Analyst

Overview

You're the person who builds the reports that tell leadership what's working and what's broken in the revenue engine. You work with Salesforce and HubSpot data daily, build dashboards in Sigma or similar BI tools, and answer questions like "why did pipeline drop 20% this month" or "which lead sources actually convert to customers." You sit between Sales, Marketing, and the executive team - translating data requests into actual analysis.

Role Snapshot

Company Context

Stage: Growth-stage (221 employees suggests Series B/C funding level)

Size: 221 employees

Growth: Actively hiring across GTM functions, which suggests expanding sales org

Market Position: Niche technical product (device intelligence/fraud detection) in a specialized but growing market - competing on technical accuracy and signal depth

GTM Reality

What You're Supporting: Fingerprint sells a technical fraud prevention platform to enterprises. The sales motion likely involves:

• Inbound leads from companies dealing with fraud issues (security teams searching for solutions)
• Outbound to e-commerce and SaaS companies that fit their ICP
• Technical evaluation periods where prospects test the product
• Multi-stakeholder deals involving Security, Engineering, Product, and Procurement

Your Job: Make sense of all the data flowing through that process - which sources generate pipeline, where deals stall, what the conversion rates look like at each stage, and how different segments perform.

Org Structure: As a 221-person company with active GTM hiring, they likely have a small RevOps team (maybe 2-4 people total). You're probably not the only analyst but you're not on a massive team either.

What You'll Actually Do

Time Breakdown

Dashboard Building (30%) | Ad-hoc Analysis (35%) | Meetings/Stakeholder Mgmt (25%) | Data Cleanup (10%)

Key Activities

• Build and maintain core funnel reports: You own the weekly/monthly pipeline reports that leadership reviews. You're updating dashboards in Sigma or Looker, making sure MQL → SQL → Opportunity → Closed-Won numbers are accurate, and investigating when things don't look right.

• Answer "why did X happen" questions: A sales leader asks why their team's close rate dropped 15% last quarter. You pull data from Salesforce, segment by rep/source/deal size, find the pattern (maybe enterprise deals are taking longer, or a specific lead source dried up), and present findings in a deck or dashboard.

• Support forecasting and planning: Before QBRs or board meetings, you pull historical data to inform quota setting, territory planning, or headcount models. You're the person who knows "our AEs averaged $450K closed last year but it took 4 months to ramp."

• Clean up messy data: Reps don't always update Salesforce correctly. Stages get skipped, close dates slip without updates, lead sources are wrong. You spend time identifying bad data, creating validation rules, and sometimes manually fixing records so your reports are trustworthy.

• Ad-hoc requests from executives: "Can you pull a list of all deals over $50K that closed in Q1 and show me what industry they're in?" These pop up constantly and take priority over your planned work.

The Honest Reality

What's Hard

• Data quality is never perfect: Salesforce data is only as good as what reps enter. You'll spend a lot of time chasing down why numbers don't match or trying to backfill missing information. It's frustrating when your analysis is questioned because the underlying data is messy.

• Competing priorities: Sales leadership wants one report, Marketing wants another, the VP wants something for the board deck, and they all want it by end of week. You have to negotiate timelines and sometimes say no, which is uncomfortable.

• You're supporting decisions but not making them: You can show that a specific lead source has a 2% conversion rate vs 15% for another, but if leadership decides to keep investing in the low-performer for strategic reasons, that's their call. Your job is insight, not authority.

• Context switching: One hour you're debugging SQL queries, the next you're in a meeting explaining funnel metrics to sales managers who aren't data-fluent, then you're building a Looker dashboard. It's mentally taxing to shift gears constantly.

What Success Looks Like

• Leadership makes faster decisions because your dashboards show them what's happening in real-time instead of waiting for end-of-quarter surprises
• Sales and Marketing teams trust your numbers and reference your reports in their weekly planning meetings
• You identify a bottleneck in the funnel (like "enterprise deals stall at legal review for 6+ weeks") and the insight leads to a process change that accelerates deals

Who You're Supporting

Primary Stakeholders:

• VP Sales / CRO: Wants pipeline visibility, forecast accuracy, and performance tracking by rep/region/segment
• Head of Marketing: Needs lead source ROI, MQL-to-SQL conversion tracking, and campaign performance data
• Sales Managers: Want rep scorecards, activity metrics, and deal health indicators
• Executive team: Require high-level dashboards for board meetings and investor updates

What They Care About:

• Accuracy: If your numbers don't match their spreadsheet or Salesforce exports, they lose trust immediately
• Speed: They need answers quickly - "can you pull this by EOD" is common
• Actionability: They don't want raw data dumps; they want "here's what's broken and here's what we should test to fix it"

Requirements

• 2-4 years experience in SaaS revenue operations, sales operations, or GTM analytics
• Strong SQL skills - you need to write queries against Salesforce/data warehouse tables regularly
• Experience with BI tools (Sigma, Looker, Tableau, or similar) building dashboards from scratch
• Deep familiarity with Salesforce and HubSpot data models - you know what standard objects/fields exist and how they relate
• Ability to translate business questions into data analysis and present findings to non-technical audiences
• Comfortable with ambiguity - you'll get vague requests like "something seems off with pipeline" and need to figure out what to investigate
• Experience working cross-functionally with Sales, Marketing, and CS teams