Product Analytics Lead

Nominal•Los Angeles, CA

About The Position

Nominal is building the software infrastructure powering the world’s most advanced hardware systems — from spacecraft and autonomous vehicles to next-generation industrial machines. Our platform ingests high-rate telemetry, validates complex autonomy software in real time, and enables engineers to iterate faster without sacrificing safety or precision. We’re a small, fast-moving team of engineers and operators who own problems end-to-end, work across disciplines, and thrive on challenges at the intersection of hardware and software. As a dual-use platform, we serve top-tier commercial and defense customers, including the U.S. Navy, United States Air Force, Shield AI, and Anduril. We’re backed by Sequoia, General Catalyst, Founders Fund, Lux Capital, and Lightspeed Ventures. Our team draws experience from SpaceX, Palantir, Anduril, Applied Intuition, and other leading companies — united by a common mission: enabling hardware engineers to push the boundaries of advanced technology with speed, safety, and precision. 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.

Requirements

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.

Nice To Haves

Experience designing or supporting experimentation / A/B testing frameworks.
Familiarity with instrumentation design and data quality enforcement.
Software engineering experience (product development, testing, CI/CD, version control).
Experience working with technically complex or industrial products.
Experience hiring, managing, and/or mentoring other analysts (formally or informally).

Responsibilities

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.