Overview
You're the technical backbone for Grafana's go-to-market teams. You build automation, integrate systems (Salesforce, HubSpot, analytics tools), create AI-powered workflows, and solve data problems that impact how sales and marketing teams operate. You work with revenue operations, sales leadership, and sometimes directly with reps to understand what's broken and build technical solutions.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | GTM/Revenue Operations Engineer (Technical) |
| Sales Motion | N/A - Internal enablement role |
| Deal Complexity | N/A - You're building for the team that closes deals |
| Sales Cycle | N/A |
| Deal Size | N/A |
| Quota (est.) | N/A - Measured on projects delivered and system uptime |
Company Context
Stage: Late-stage (1,774 employees, established product)
Size: 1,774 employees
Growth: Hiring for AI/automation capabilities suggests they're scaling GTM operations and investing in efficiency
Market Position: Leader in observability with strong open-source roots, competing against Datadog, New Relic, Dynatrace, and Splunk
GTM Reality
The Team You Support:
- Sales teams selling Grafana Cloud to enterprises and mid-market companies
- Marketing teams running campaigns and generating leads
- Customer success teams managing renewals and expansions
- Revenue operations teams trying to get clean data and forecasts
Common Problems You Solve:
- Salesforce workflows are manual and slow
- Data doesn't sync properly between systems
- Marketing wants to automate lead scoring or routing
- Sales leadership wants better pipeline visibility
- Teams are doing repetitive tasks that could be automated
- AI/LLM use cases for internal productivity (summarizing calls, drafting emails, analyzing deals)
What You'll Actually Do
Time Breakdown
Building/Coding (40%) | Integrations/Systems Work (30%) | Stakeholder Management (20%) | Firefighting (10%)
Key Activities
- Building AI Agents & Automation: You create multi-agent workflows, LLM-powered tools for internal teams, and automation that reduces manual work. Could be anything from AI call summaries to automated lead enrichment to chatbots that answer rep questions.
- System Integrations: You connect Salesforce, HubSpot, data warehouses, analytics platforms, and various SaaS tools. You write scripts, build APIs, configure webhooks, and maintain data pipelines.
- Troubleshooting Data Issues: Sales says their dashboards are wrong. Marketing says leads aren't routing. You dig through logs, trace data flows, and fix what's broken.
- Stakeholder Conversations: You talk to rev ops leaders, sales managers, and sometimes individual reps to understand what they need. You scope projects, explain what's possible technically, and push back when requests are vague or unrealistic.
- Documentation & Handoffs: You document what you build so others can maintain it. You train ops team members on new workflows and tools.
The Honest Reality
What's Hard
- Vague Requirements: Stakeholders say "we need better automation" but can't articulate exactly what that means. You spend time digging to understand the actual problem.
- Legacy Technical Debt: You inherit systems that were set up years ago by someone who's gone. Code isn't documented. Workflows are fragile. You have to reverse-engineer things before you can improve them.
- Competing Priorities: Sales wants feature X tomorrow. Marketing needs Y urgently. Rev ops has a critical data issue. You're constantly triaging and managing expectations.
- System Limitations: The tools (Salesforce, HubSpot, etc.) have constraints. You can't always build what's requested. You get creative with workarounds or explain why something isn't possible.
- AI Hype vs Reality: Everyone wants "AI-powered" solutions but doesn't understand the limitations. You manage expectations about what LLMs can actually do reliably.
- On-call Nature: When systems break (and they will), it impacts revenue teams immediately. You get pinged to fix things fast.
What Success Looks Like
- You ship automation that saves GTM teams X hours per week (measurable time savings)
- Your AI agents get adopted and used regularly by reps or ops teams
- System integrations run reliably with minimal manual intervention
- Pipeline/forecast data is accurate and stakeholders trust the dashboards
- Fewer firefighting emergencies because systems are stable and well-documented
Who You're Working With
Primary Stakeholders:
- VP/Director of Revenue Operations (your main partner, defines priorities)
- Sales Managers/Leadership (they request features and complain when things break)
- Marketing Operations (need help with lead routing, scoring, attribution)
- Customer Success Ops (want automation for renewals, health scoring)
What They Care About:
- Can you solve this quickly? (They have urgent needs)
- Will it break our existing setup? (They're nervous about changes)
- Can we scale this as we grow? (They don't want to rebuild every 6 months)
- How much will this cost in tool spend? (Budget constraints)
Requirements
- Strong coding skills (Python, JavaScript, or similar - you're writing real code, not just configuring tools)
- Experience with GTM systems like Salesforce, HubSpot, or similar CRMs/marketing automation platforms
- Familiarity with AI/LLM workflows (building agents, prompt engineering, understanding model limitations)
- API integration experience (RESTful APIs, webhooks, authentication patterns)
- Data modeling and pipeline experience (SQL, ETL concepts, data warehouses)
- Ability to talk to non-technical stakeholders and translate vague requests into technical specs
- Comfort with ambiguity and changing priorities (this isn't a well-defined backlog)
- Track record of shipping projects end-to-end, not just building prototypes