Senior Product Analytics Engineer

About Novellia
Novellia is the first and only company that lets anyone in the U.S. gain access to nearly a decade of their health data in under 30 seconds — 100% free. All your health records, across every doctor, in one place, always up to date.

We are the only patient-powered real-world data platform delivering comprehensive, patient-authorized longitudinal health insights to accelerate biopharma innovation. Unlike traditional RWD providers who deliver fragmented institutional data, we empower patients to access 20+ years of their health records, then transform these complete health journeys into fit-for-purpose datasets for evidence generation, regulatory submissions, and market access. We are growing 5x year over year, have raised close to $30M in funding, and are backed by tier-1 investors including Spark Capital, Khosla Ventures, and Bling Capital.

Working with the world's top researchers, we turn health insights into life-changing action for millions of people around the world.

About the Role
We're looking for a Senior Analytics Engineer who is genuinely obsessed with understanding how people use products. We need someone who partners with product and engineering to figure out what we should be measuring in the first place, and then builds the infrastructure to measure it reliably, rather than just someone who builds pipelines.

You've worked at a consumer company before and understand what it means to chase PMF. You've dug into retention curves, built funnel analyses, and identified the moments in a product that actually move the needle. You know that the hardest part often isn't the SQL: it's asking the right question.

In this role, you'll own analytics engineering as it relates to product and growth. Product and product design are your primary stakeholders. You are their data partner and a peer in those conversations: someone who steers roadmap discussions, pushes back on assumptions, and makes sure the right questions are being asked before anything gets built. You'll define what good product analytics looks like at Novellia, back into the event tracking and data models needed to get there, and build the clean, reliable infrastructure that powers product and growth decisions. You'll also work alongside analytics engineers on the team who own clinical and research data, so collaboration and clear ownership boundaries matter here.

You will report directly to the Manager, Data and Analytics. This is a senior individual contributor role. You will be the internal authority on product analytics, set the standard for how we instrument and measure our product, and influence product direction through the quality and clarity of your work. You'll also be working with one of the most interesting datasets in consumer health: longitudinal patient records spanning years of real health history across doctors and health systems, at a scale that is genuinely rare for a company at this stage. The questions you get to ask here are not the same ones you'd find at a typical consumer startup. We're a small team, which means your work has a direct impact from day one.

What Success Looks Like (First 6–12 Months)
We have a clear picture of what our product metrics and funnels should look like, and our event tracking is built to support them.

Product and product design treat you as a core member of the team, not a service. You are in the room when features are being shaped, not just asked for numbers after they ship.

We have a clear, shared understanding of our core product and growth metrics: activation, engagement, retention, and what drives each.

Our dbt models are clean, documented, and built so that anyone can build on them.

We're making product decisions grounded in data, not instinct.

Responsibilities
Serve as the dedicated data partner for product and product design: join roadmap planning, weigh in on feature scoping, and make sure the right questions are being asked before anything gets built.

Own product analytics instrumentation: audit existing tracking, identify gaps, and work with engineers to close them.

Build and maintain dbt models that serve as the foundation for product, growth, and operational reporting.

Design and maintain dashboards and self-serve tools that help PMs and leadership answer their own questions.

Drive our understanding of engagement, retention, and activation and surface insights that inform product decisions.

Identify leading indicators of product-market fit and help the team track them over time.

Build and maintain scalable data pipelines across our data warehouse, scoped to product and user data.

Collaborate closely with the analytics engineers who own clinical and research data, coordinating on shared infrastructure and data standards without stepping on each other's domains.

Proactively surface data quality issues within the product and growth domain and establish standards that make our data trustworthy.

Requirements
4+ years of experience in analytics engineering or a closely related data role.

Experience at a consumer-facing company: you understand how consumers use products and what metrics actually matter for growth.

Startup experience: you know how to operate when priorities shift and there is no playbook.

Strong SQL and dbt skills; you can build clean, modular, well-documented models.

Hands-on experience with Mixpanel, including working with engineers to define and implement event tracking.

Proven track record of working directly with product managers and designers, not just reporting to them. You have shaped what gets built, not just measured it.

Strong intuition for engagement and retention: you have done funnel analysis, cohort analysis, and retention modeling before.

Experience with our stack or something close to it: BigQuery, dbt, Stitch, and Hex. We'll continue to evolve our tooling over time, and you'll have a real voice in those decisions and a hand in implementing them.

Clear communicator who can translate between technical and non-technical stakeholders without losing nuance.

High ownership mindset: you notice what is broken and fix it without waiting to be asked.

Comfortable using AI tools to move faster: writing SQL, exploring data, documenting your work, whatever gets you there.

Experience designing or supporting A/B tests and interpreting experiment results.