# Senior Manager, Revenue Operations & Analytics

**Company:** [VGS](https://hotfix.jobs/companies/vgs)
**Location:** Remote
**Role:** Revenue Operations
**Salary:** $130k – $190k/yr
**Experience:** 5+ years
**Skills:** Salesforce, Sigma, SQL, Fivetran, Looker, Tableau, Data Pipelines, Revops, Sales Ops, Data Analytics
**Posted:** 2026-06-11

> Own end-to-end GTM and revenue data integrity. Build dashboards, manage Salesforce/Sigma pipelines, run usage analytics, and resolve billing disputes with data-backed insights.

## Job Description

## Responsibilities

### Revenue Intelligence & Reporting
- Own and maintain the tracking of company, client, and product metrics and KPIs
- Leverage AI to build out business and client performance and insights from the VGS data
- Run daily billable usage analysis to proactively flag revenue anomalies before they become billing disputes
- Translate complex data sets into actionable narratives for the leadership team

### Data & Systems Architecture
- Partner with Data Engineers to manage and optimize the flow of data through Fivetran, Salesforce, and Sigma
- Act as the bridge to Engineering: Translate business requirements into technical specs and navigate data backfills or pipeline failures
- Directly manage Salesforce hygiene and the implementation of outbound/growth tools like Clay.ai

### Commercial Forensic Operations
- Troubleshoot and investigate line item analysis to resolve client issues or provide additional insights
- Own the billing feedback loop: Flag overages, advise Account Managers on contested invoices, and ensure billing logic matches legal commitments

## Requirements
- 5+ years in RevOps, Sales Ops, or Data Analytics
- Expert Level: Salesforce (Admin preferred) and Sigma (or similar BI like Looker/Tableau)
- Experience with Data pipelines is a massive plus; comfortable writing SQL and investigating data pipelines
- Experience with billing and usage based models
- Enjoy linking Sales, Finance, and Engineering
- Confidence to "hold the line" on a billing dispute with data-backed proof
- Believe a visualization is only as good as the SQL behind it; look for context and "data smell" before refreshing

## Nice-to-Haves
- Data Driven mindset: curious about underlying drivers to the data story

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