Overview
You'll build product for DualEntry's AI-native ERP platform that automates core accounting functions. You work with engineering, design, and go-to-market teams to ship features that handle revenue recognition, contract management, reconciliations, and financial reporting for enterprise software companies and e-commerce brands. The new Head of Product (ex-Cockroach Labs) just joined, so you're part of defining the product org structure.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Product Manager (B2B SaaS) |
| Primary Focus | Feature development for AI-powered accounting automation |
| Team Structure | Report to Head of Product, collaborate with engineering and design |
| Shipped Work | Revenue recognition tools, AP/AR automation, reconciliation features |
| Product Stage | Post-Series A (actively building core platform) |
| Customer Base | Startups to NYSE companies, including high-velocity businesses |
Company Context
Stage: Series A ($90M raised, led by Khosla Ventures and Lightspeed)
Size: 56 employees (actively hiring across product/engineering)
Growth: Recent major funding round, hiring for multiple product roles, new Head of Product from Cockroach Labs
Market Position: Category creator - "first AI-native ERP built after ChatGPT," competing against legacy systems (likely NetSuite, Intacct, QuickBooks Enterprise) that are retrofitting AI
What You'll Actually Do
Time Breakdown
Customer Research (25%) | Feature Definition (30%) | Engineering Collaboration (30%) | Internal Alignment (15%)
Key Activities
- Customer Research: Talk to finance teams at customers and prospects to understand workflow pain points. You're figuring out where AI automation actually saves time vs. where it creates new problems. Expect to learn a lot about accounting operations if you don't know it already.
- Feature Specification: Write PRDs for capabilities like automated revenue recognition, anomaly detection in GL entries, or contract-to-cash workflows. You're defining how AI should handle edge cases in financial processes, which means a lot of "what should the system do when..." scenarios.
- Eng Collaboration: Work with engineering to scope features, review designs, and make tradeoff decisions. You're balancing building fast (Series A velocity) with building right (finance teams need accuracy). Lots of async communication and technical design reviews.
- Integration Strategy: Prioritize which of the "13,000+ native integrations" actually matter. Most customers use 5-10 key systems (Salesforce, Stripe, banking platforms). You're figuring out which integrations unlock revenue vs. which are just nice-to-have.
The Honest Reality
What's Hard
- Domain Complexity: If you're not from finance/accounting, you're learning revenue recognition rules, multi-currency handling, audit requirements, and SOX compliance on the job. Finance teams have zero tolerance for errors in their books.
- AI Uncertainty: You're defining how AI should work in high-stakes financial processes. There's no playbook for "AI-native ERP" - you're making product calls about automation confidence levels, human-in-the-loop workflows, and when to override AI suggestions.
- Early-Stage Chaos: New Head of Product, fast hiring, Series A growth mode. Product processes are getting defined in real-time. You might be the first PM for your domain, which means high autonomy but also figuring things out yourself.
- Customer Sophistication Variance: You're serving both startups and NYSE companies. A $140M ARR company running on a one-person finance team has different needs than a traditional enterprise CFO. Product has to work for both.
What Success Looks Like
- You ship features that customers adopt quickly and reduce manual work (measured in hours saved per month)
- Finance teams trust the AI automation enough to reduce their oversight - you see usage metrics showing less manual review over time
- Integration adoption increases - customers connect more of their systems and centralize workflows in DualEntry
- You maintain accuracy and compliance - no customer errors that force manual corrections or audit issues
Who You're Building For
Primary Users:
- Controllers and accounting managers at mid-market companies (20-500 employees)
- Finance teams at high-growth startups (often very lean - 1-3 people handling significant volume)
- CFOs evaluating ERP consolidation to reduce system sprawl
What They Care About:
- Accuracy: Financial data errors are career-ending for them. AI needs to be right 99.9%+ of the time.
- Time Savings: They're drowning in manual reconciliations, month-end close processes, and revenue recognition entries. Need proof that automation actually works.
- Audit Trail: Everything needs clear documentation for auditors and compliance teams.
- System Consolidation: They're using 5-10 tools today. They want fewer logins and less data sync.
Requirements
- 2-4 years product management experience in B2B SaaS (fintech or accounting software background is a plus but not required)
- Strong technical understanding - you need to grasp how AI models work and where they fail
- Willingness to learn accounting/finance domain expertise quickly (or bring it already)
- Comfort with ambiguity - you're defining the category, not following a proven playbook
- Ability to talk to customers and translate finance workflows into product requirements
- Experience shipping features in early-stage environments where you build the plane while flying it