Senior Product Analyst

<div class="content-intro"><p>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 &amp; Insure platform, and continue to grow and expand our team, our partnerships and our revenue.</p></div><p>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 &amp; engineers.</p> <p><strong>Responsibilities:</strong></p> <ul> <li>Definition of North Star KPIs</li> <li>Build the experimentation muscle. Run real A/Bs on the parts of the product that matter. Kill features that don't earn their keep.</li> <li>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.</li> <li>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.</li> <li>Become the person leadership pings when they need to understand something quickly and correctly.</li> </ul> <p><strong>Requirements:</strong></p> <ul> <li>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.</li> <li>Strong SQL - you've shipped the queries that quietly power half the company's decisions.</li> <li>Comfort in Python for analysis and automation.</li> <li>Experience with a transformation/pipeline tool (DBT or similar) and a clean modeling discipline.</li> <li>Real product analytics chops - funnels, retention, experimentation, segmentation. You can explain when <em>not</em> to A/B test as well as when to.</li> <li>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.</li> <li>High curiosity and a strong bias toward action. Founding-team energy.</li> </ul> <p><strong>Major plus:</strong></p> <ul> <li>DBT specifically.</li> <li>Strong Python (stats, modeling, light ML).</li> <li>PostHog experience.</li> <li>Background in cybersecurity, channel sales, or B2B2B SaaS.</li> </ul>