Product Analytics Engineer

Product Analytics Engineer About Klarity Klarity (YC S18) is the Enterprise Instinct platform. TL;DR: Series B, $91M raised, 7x growth last year. Enterprises still rely on consultants to figure out how they operate – months of work, millions spent, and outdated before the ink dries. We built a better way. Our AI discovers how work actually happens across every team and application, Structures it into a living Context Graph, and Improves it continuously. ServiceNow mapped 900+ processes in 9 days. DoorDash captured 3,800+ finance operations in 14 weeks, replacing a 2-year engagement. That's not a project. That's compounding intelligence. OpenAI, Google, DoorDash, and Stripe use Klarity to transform how they transform. We shipped GPT-4 document chat within 12 hours of OpenAI's API launch. Speed and quality aren't a tradeoff here – they're the standard. THE ROLE Generative AI is rapidly commoditizing SQL queries, dashboards, and traditional data analytics. Anyone can write a query now. What AI can't replace is the judgment to know what questions matter, the instinct to connect a qualitative signal from a customer call to a quantitative pattern in usage data, and the conviction to walk into a room and say "here's what we should build next, and here's why." We're looking for a Product Analytics Engineer who transforms how our product team makes decisions. This is not a service function that waits for data requests. You are embedded in the product organization—working side-by-side with our Director of Engineering, Head of Design, and CTO—proactively pushing insights and recommendations that shape what we build and how we iterate. You operate across the full spectrum of product intelligence: quantitative analysis of user behavior and feature performance, qualitative analysis of customer conversations and feedback using generative AI, and the strategic judgment to synthesize both into clear product recommendations. You don't provide charts. You provide decisions. WHO YOU ARE - Maps business outcomes to data, not data to dashboards. You start with the product question, not the query. You understand what success looks like for a feature, define the metrics that prove it, instrument the events to capture them, and interpret the results in the context of business outcomes. When someone asks "how is this feature doing?", you don't just pull numbers—you tell them what the numbers mean and what to do next. - AI-native analyst who multiplies their reach. You use generative AI tools to do what previously required entire teams. You analyze hundreds of Gong call transcripts to surface recurring pain points. You synthesize NPS feedback, support tickets, and user interviews at scale using LLMs. You treat AI as a force multiplier for qualitative research, not just a SQL copilot. - Proactive, not reactive. You don't wait for a Jira ticket asking for a chart. You see a drop in activation, dig in before anyone asks, and bring a hypothesis and recommendation to the next planning meeting. You set up alerts, monitoring, and recurring analyses that surface problems and opportunities before the team even knows to look. - Thinks in experiments, not reports. You design and analyze A/B tests, feature experiments, and rollout strategies. You understand statistical significance, but you also know when to trust directional signal over perfect data. You help the product team build conviction through rapid experimentation, not endless analysis cycles. - Bridges qualitative and quantitative fluently. You move seamlessly between analyzing PostHog funnels and reading customer interview transcripts. You can spot when quantitative data contradicts what users say (and vice versa) and synthesize both into a coherent story. The best insights come from triangulating across data types, and you do this instinctively. - Brings energy that elevates the product team. You see messy data and ambiguous signals as puzzles to solve. Your enthusiasm for finding the ... (truncated, view full listing at source)