Deal Desk Analyst

What You'll Do

Key Outcomes: Success in this role will be measured by your ability to:

• Own day-to-day deal support and use it to raise the floor. Review in-flight order forms for non-standard terms and route exceptions before they hit Legal or provisioning. Beyond the throughput, you’ll help guide quality standards, exception taxonomy, and intake criteria to build from patterns and create cleaner systems and processes over time.
• Turn post-close opportunity review into a systemic feedback loop. Audit closed-won deals for accuracy between Salesforce and contract data, and surface reconciliation gaps before they hit billing or provisioning. Use those findings to diagnose root causes — whether that's a SFDC field mapping issue, an Ironclad template gap, or a rep behavior — and drive the fixes upstream so the same error doesn't recur.
• Build and own the Deal Desk operating system. Design and maintain the runbooks, approval matrices, intake workflows, and deal-type documentation that let the team scale. The goal is a Deal Desk that's self-serve by default and escalation-by-exception.
• Contribute to the infrastructure that replaces the workarounds. Translate operational pain points into structured, credible input for engineering and RevOps. The best analysts in this role won't just document problems; they'll help design the systems that make them disappear.

What we're looking for:

• 4-6 years in Deal Desk, Revenue Operations, Sales Operations, or a related commercial function, with direct experience in contract review, deal structuring, or order management.
• Salesforce proficiency — comfortable navigating Opportunity and related child objects, pulling reports, and spotting data quality issues. You understand how SFDC connects to the broader quote-to-cash toolset.
• Systems thinker with a builder's bias. When you encounter a recurring problem, your instinct is to figure out why it keeps happening and fix the process, not just close the ticket. You document what you learn, build the thing that prevents the next ten instances, and measure whether it worked.
• Comfortable operating in ambiguity. Both in systems and in