Product Analytics Lead, Nominal

• 5–10 years of experience in product analytics, analytics engineering, or data-adjacent product roles with significant ownership
• Proven experience designing analytics systems in partnership with engineering—including selecting, implementing, and using analytics and BI tools (e.g., Amplitude, PostHog, Heap, Looker, Tableau, Metabase)
• Strong command of SQL and comfort working with data programmatically (e.g., Python)
• Experience modeling product behavior, defining metrics, and working with event-based data
• Strong product intuition and systems thinking — you care about how metrics shape behavior and decisions
• Comfortable operating in ambiguity and making informed tradeoffs
• Clear, concise communicator who can engage engineers, product managers, and leadership
• High standards, high ownership, and strong personal integrity

Desirable

• We’re looking for a Product Analytics Lead to define how we measure, understand, and improve our product as we scale
• This role owns analytics end-to-end: what we measure, how we measure it, and how insights get used
• You will design our analytics architecture, select and implement tooling in partnership with engineering, and ship analytical products that directly influence product direction and company strategy
• This is a hands-on, high-ownership role
• You’ll work closely with product, engineering, and customers to develop a deep, systems-level understanding of how Nominal is used in the real world — and translate that understanding into durable metrics, models, and decision frameworks
• Own Product Analytics Architecture
• Lead analytics tooling selection and architecture decisions, evaluating build vs. buy and making pragmatic tradeoffs for a fast-moving startup. Select capabilities that are applicable both on and off-cloud (air-gapped) to support Nominal’s full breadth of deployment environments
• Partner with engineering to implement, integrate, and scale analytics systems that are reliable, observable, and easy to extend
• Establish standards for instrumentation, metrics, experimentation, and analytical rigor across the product
• Build Durable Analytics Foundations
• Design and maintain clean, well-structured analytical models that power product insights, experimentation, and reporting
• Ensure analytics outputs are trustworthy through testing, documentation, and monitoring
• Create shared definitions and abstractions that enable self-serve analysis without sacrificing correctness
• Ship High-Leverage Analytical Products
• Build and own dashboards, tools, and analyses that product teams and leadership rely on to make decisions
• Translate ambiguous product questions into concrete analytical approaches
• Balance speed and depth — delivering timely insights while preserving long-term maintainability
• Define Metrics That Matter
• Partner with product and leadership to define clear, opinionated metrics for product health, user behavior, and outcomes
• Reduce ambiguity by creating a consistent, shared understanding of how success is measured
• Evolve metrics as the product and customer base mature
• Develop Deep Product & User Understanding
• During your first few months, conduct user interviews and customer conversations to develop a first-hand understanding of how Nominal is used in production environments
• Help engineering leaders combine qualitative insights with quantitative analysis to inform their product strategy and prioritization
• Act as a connective layer between customer reality (the data) and internal decision-making
• Communicate With Precision
• Turn complex analyses into clear, defensible narratives for product teams and executives
• Influence decisions through evidence, not volume
• Raise the analytics bar through documentation, examples, and thoughtful critique