Principal Data Analyst, ClickUp

• Significant experience in analytics, business intelligence, data science, or product analytics, including strong experience partnering with Product teams in a B2B SaaS or product-led growth environment
• Advanced SQL skills and strong familiarity with modern cloud data and analytics tooling such as Snowflake, dbt, Amplitude, Segment, Hex, Tableau, Looker, or similar platforms
• Working proficiency in Python for analysis, automation, or experimentation support
• Strong product analytics judgment, including experience with event instrumentation, funnel analysis, cohort analysis, retention, feature adoption, and experimentation
• Demonstrated ability to translate broad, ambiguous questions into structured analysis plans and actionable recommendations for senior stakeholders
• Excellent written and verbal communication skills, with a track record of making complex findings easy for product and executive audiences to act on
• Clear examples of using AI, automation, or agentic workflows to improve analytics speed, scale, or quality while maintaining strong QA and decision discipline
• A high-ownership working style with strong prioritization skills and a track record of independently driving high-impact work

Desirable

• We’re hiring a highly influential product analytics leader who can turn ambiguous questions into sharp insight, scalable measurement, and recommendations that directly shape what we build. This person will partner closely with Product, Engineering, Design, and Growth to raise the bar on decision quality and establish a more AI-native analytics operating model
• Own the product analytics agenda across product usage, activation, feature adoption, retention, and expansion, and translate open-ended business questions into structured analyses and clear recommendations
• Partner with Product, Engineering, and Design to define success metrics early, improve instrumentation quality, and ensure important product surfaces are measurable from launch
• Build reusable analysis frameworks, semantic layers, metric definitions, and self-serve resources that help product teams answer routine questions faster and more consistently
• Apply AI-first methods across the analytics workflow, using large language models, coding agents, and automation for tasks like query drafting, QA, validation, documentation, and first-pass synthesis while keeping human judgment at the center of final recommendations
• Design and interpret experiments, observational analyses, and trend investigations, including situations where data is incomplete or traditional experimentation is not feasible
• Surface meaningful patterns in behavioral, subscription, and product data to influence roadmap choices, prioritization, and executive decision-making
• Create monitoring and alerting approaches that reduce manual reporting cycles and make it easier to spot product opportunities, risks, and anomalies earlier
• Mentor analysts and elevate team practices around analytical rigor, AI-assisted workflows, insight storytelling, and stakeholder communication