Senior Product Analyst

Israel·Tel Avivsenior

Data AnalystData

0 views0 saves0 applied

Apply Now

Quick Summary

Key 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.

Requirements Summary

3+ years in product analytics or product data science, ideally at a SaaS company. PLG, SMB, or channel/partner-led experience is a real plus.

Technical Tools

Data AnalystData

Established in 2022, Guardz rapidly emerged as a noteworthy player in the cybersecurity sphere, securing $85M in funding and rallying a dedicated team of 100 industry professionals. Our vision is to foster a safer digital landscape for small and medium businesses across the globe. To this end, we introduced our comprehensive all-in-one Secure & Insure platform, and continue to grow and expand our team, our partnerships and our revenue.

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

~1 min read

→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

~1 min read

3+ 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.