RevOps Data Analyst

Overview

You're the data person for Tive's entire Go-To-Market organization—Sales, Marketing, and Customer Success. You'll spend your days in Salesforce, building dashboards, investigating metric discrepancies, and translating business questions into SQL queries. You report to the Global Director of RevOps and work with stakeholders who need data to make pipeline, hiring, and budget decisions.

Role Snapshot

Company Context

Stage: Series D ($500M+ valuation per poster's headline)

Size: 251 employees

Growth: Adding RevOps headcount suggests scaling GTM motion, likely expanding into larger enterprise accounts

Market Position: Established player in supply chain visibility/tracking—competing in a space with both legacy logistics providers and newer IoT tracking companies

GTM Reality

What Tive Sells: Hardware trackers + software platform for real-time shipment monitoring. Customers are in food & beverage, life sciences, high-value goods, and logistics. This means:

• Sales cycle involves both software evaluation AND hardware/device logistics
• Deals likely have procurement complexity (hardware + subscriptions)
• Customer Success must monitor device performance, not just software adoption

Your Stakeholders:

• Sales team tracking pipeline, conversion rates, rep performance
• Marketing measuring campaign ROI, lead quality, attribution
• CS tracking retention, expansion, health scores
• RevOps Director who needs exec-ready reporting

Data You'll Work With:

• Salesforce (CRM data, pipeline, won/lost analysis)
• Marketing automation (likely Marketo/HubSpot—lead sources, campaign performance)
• CS platform (Gainsight or similar—NRR, churn risk, product usage)
• Possibly product usage data (device telemetry, platform logins)

What You'll Actually Do

Time Breakdown

Dashboard Building (30%) | Ad-Hoc Analysis (35%) | Data Cleanup (20%) | Meetings (15%)

Key Activities

• Building and Maintaining Dashboards: You own the reporting infrastructure. Sales VPs want pipeline coverage by segment, Marketing wants funnel conversion rates, CS wants retention cohorts. You're in Tableau/Looker/Salesforce Reports building these, then updating them when definitions change (which they will).

• Ad-Hoc Analysis Requests: "Why did our demo-to-close rate drop last quarter?" "Which marketing channels drive the highest LTV customers?" "What's our average time-to-close by deal size?" You field 5-10 of these per week. Some take 30 minutes, others require digging through messy data for days.

• Data Quality Management: Reps don't always update Salesforce correctly. Lead sources get mis-tagged. Duplicate accounts exist. You spend significant time identifying data issues, documenting them, and either fixing them yourself or getting ops to create processes/validation rules.

• Metric Definition & Documentation: When someone says "pipeline," do they mean all open opps or just Stage 3+? Does "close rate" include churned-then-won-back deals? You create and maintain the source of truth for how metrics are calculated so everyone's looking at the same numbers.

• Forecasting Support: At a Series D company, board-level forecasting matters. You help RevOps build models for quota attainment, hiring plans, and revenue projections. This gets intense around month/quarter-end.

The Honest Reality

What's Hard

• Everyone thinks their request is urgent: Sales needs pipeline analysis by EOD. Marketing needs campaign ROI by tomorrow's meeting. CS needs churn deep-dive for exec review. You're constantly prioritizing and managing expectations about turnaround time.

• Data is messy and you inherit the mess: Salesforce has years of accumulated custom fields, old workflows, and inconsistent data entry. Before you can answer most questions, you have to clean/normalize the data. This is not glamorous work.

• You're explaining the same concepts repeatedly: Why the numbers in the CRM don't match the spreadsheet someone downloaded last week. Why we can't track attribution perfectly when reps manually enter lead sources. Why closed-won amounts don't match invoicing (different systems, timing, discounts).

• Scope creep is constant: "Can you also pull data from our finance system?" "Can you learn our product analytics tool?" "Can you help set up our new MarTech stack?" The role expands into whatever data problems exist.

• You're a cost center: Unlike quota-carrying roles, your impact is indirect. You have to advocate for data quality initiatives and tooling investments without direct revenue attribution.

What Success Looks Like

• Stakeholders stop arguing about numbers: When Sales, Marketing, and CS all reference your dashboards in meetings and agree on the metrics, you've won.

• You catch problems before leadership asks: You notice conversion rates declining and flag it proactively with analysis, rather than scrambling when someone asks "what happened?"

• Requests get faster: As you build out automated reporting and clean up data, the time from "I need X" to "here it is" shrinks from days to hours.

Who You're Supporting

Primary Stakeholders:

• RevOps Director (your boss—needs exec-ready analysis)
• Sales leadership (VPs, RVPs—need pipeline visibility, rep performance)
• Marketing team (need funnel metrics, campaign ROI)
• CS leadership (need retention/expansion metrics)
• Finance (need forecast accuracy, bookings vs. revenue reconciliation)

What They Care About:

• Accuracy: Numbers can't be wrong. Ever. Board decks and comp plans depend on your data.
• Speed: They're making decisions in real-time and need analysis quickly.
• Context: Not just "here's the number" but "here's what changed and why."
• Proactivity: Surfacing trends before they become problems.

Requirements

• Strong SQL skills—you'll write queries daily to pull data from Salesforce, marketing automation, and other GTM systems
• Salesforce reporting experience (reports, dashboards, potentially SOQL)
• BI tool experience (Tableau, Looker, Power BI, or similar)
• Excel/Sheets power user—pivot tables, VLOOKUP, complex formulas for ad-hoc analysis
• Experience with GTM metrics (pipeline coverage, conversion rates, CAC, LTV, NRR)
• Ability to communicate technical findings to non-technical stakeholders
• Comfortable with ambiguity—you'll often get vague requests and have to clarify what they actually need
• Experience in B2B SaaS or hardware/software hybrid companies is helpful (understanding how deals flow)
• Bonus: Python/R for statistical analysis, experience with product analytics tools, familiarity with supply chain/logistics domain