Overview
You build AI-powered workflows and internal tools that connect Rubrik's data infrastructure to their sales organization. This means pulling data from Salesforce, product usage systems, and other sources, then building agentic workflows (think: automated lead scoring, next-best-action recommendations, account intelligence agents) that surface in reps' daily workflows. You work with GTM leadership to identify bottlenecks, then engineer solutions.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Revenue Operations / GTM Data & AI |
| Sales Motion | Internal tooling - supporting enterprise sales org |
| Deal Complexity | N/A - building internal systems |
| Sales Cycle | N/A |
| Deal Size | N/A |
| Quota (est.) | No revenue quota - measured on adoption and impact of tools built |
Company Context
Stage: Late-stage / Public (4900+ employees, mature infrastructure)
Size: 4900 employees
Growth: Large enterprise sales org with established GTM motion - now investing in AI-powered efficiency
Market Position: Data security/backup leader competing with Veeam, Commvault, Cohesity
What You'll Actually Do
Time Breakdown
Data Engineering (35%) | AI/Agent Development (30%) | Stakeholder Meetings (20%) | Documentation/Testing (15%)
Key Activities
- Data Pipeline Work: Writing SQL queries to pull account, opportunity, and product usage data from Salesforce, Snowflake, and internal databases. Cleaning and structuring data for AI consumption.
- Building Agentic Workflows: Deploying AI agents (likely LangChain, LlamaIndex, or similar frameworks) that automate tasks like: "Which accounts show usage patterns indicating churn risk?" or "Generate personalized outreach messages based on account activity."
- Sales Tool Integration: Embedding your AI outputs into tools reps actually use - Salesforce dashboards, Slack bots, Chrome extensions. You're measured on adoption, not just building something.
- Requirements Gathering: Meeting with sales managers, enablement, and ops to understand where reps are wasting time or missing opportunities. Translating fuzzy problems into technical specs.
- Performance Monitoring: Tracking whether your tools are being used and if they're actually helping (e.g., "Did lead scoring improve conversion rates?" or "Are AEs acting on the recommendations?").
The Honest Reality
What's Hard
- Messy Data: Rubrik's data lives in multiple systems with inconsistent formats. You'll spend significant time just getting clean, usable datasets before any AI work happens.
- Adoption Challenges: Building a tool is the easy part. Getting 200+ sales reps to actually change their workflow and trust your AI recommendations is harder. You'll need to evangelize, train, and iterate based on feedback.
- Vague Requirements: Sales leaders will say "we need better insights" without being able to articulate what that means. You need to translate business problems into technical solutions while managing expectations about what AI can realistically do.
- Moving Targets: GTM priorities shift. The tool you're building this quarter might get deprioritized next quarter when leadership changes strategy.
What Success Looks Like
- You ship an AI agent that 50%+ of the sales team uses weekly within 3 months of launch
- Your lead scoring model demonstrably improves conversion rates by X% (tracked in Salesforce)
- Sales managers can point to specific deals that moved faster because of intelligence your tools surfaced
Who You're Supporting
Internal Stakeholders:
- Account Executives (enterprise sales reps selling $200K+ deals)
- Sales Development Reps (prospecting and qualifying)
- Sales Engineers (running technical demos and POCs)
- Revenue Operations (who own Salesforce and reporting)
- Sales Leadership (VPs who want dashboards and predictive analytics)
What They Care About:
- Saving time on manual research and admin work
- Getting actionable insights without having to dig through Salesforce
- Tools that actually work reliably (not science experiments that break)
- Not adding more tools to their stack unless they replace something
Requirements
- Strong SQL and data manipulation skills (Python/pandas essential)
- Experience with AI/LLM frameworks (LangChain, LangGraph, or similar agent frameworks)
- Familiarity with Salesforce data model and GTM tools
- Can translate business problems into technical solutions without hand-holding
- Comfortable presenting to sales leadership and explaining technical concepts in business terms
- Prior experience in RevOps, sales analytics, or GTM data roles at a B2B SaaS company