This is a founding role. You won't inherit a dashboard graveyard or a half-built warehouse - you'll design the modeling layer, choose the tools, set the methodology, and decide what "good" looks like. You'll be the first product analyst, reporting to a centralized data team, and working directly with product leadership, go-to-market, UX designers & engineers.

Responsibilities:

• Definition of North Star KPIs
• Build the experimentation muscle. Run real A/Bs on the parts of the product that matter. Kill features that don't earn their keep.
• Map the customer journey end-to-end - from signup to first deployment to first protected tenant to expansion - and tell us where the friction is, with numbers.
• Analytics infrastructure: warehouse modeling, transformation layer, product analytics tool, BI. You'll have a strong opinion on the stack and the autonomy to build it.
• Become the person leadership pings when they need to understand something quickly and correctly.

Requirements:

• 4+ years in product analytics or product data science, ideally at a SaaS company. PLG, SMB, or channel/partner-led experience is a real plus.
• Strong SQL - you've shipped the queries that quietly power half the company's decisions.
• Comfort in Python for analysis and automation.
• Experience with a transformation/pipeline tool (DBT or similar) and a clean modeling discipline.
• Real product analytics chops - funnels, retention, experimentation, segmentation. You can explain when not to A/B test as well as when to.
• An AI-native working style. You build small agents and prompts to do the boring parts of your job. You assume the answer is "this should be automated" until proven otherwise.
• High curiosity and a strong bias toward action. Founding-team energy.

Major plus:

• DBT specifically.
• Strong Python (stats, modeling, light ML).
• PostHog experience.
• Background in cybersecurity, channel sales, or B2B2B SaaS.