Product Analyst

Spellbook

1d•Remote

About The Position

Spellbook is hiring a Product Analyst to improve how we measure, understand, and act on product performance. In this role you’ll build a single source of truth for product metrics and partner closely with Product and Engineering to translate ambiguous questions into crisp analysis, dashboards, and recommendations.

Requirements

You have 4+ years in product analytics / product data / data analytics in a SaaS product environment.
You have strong SQL (complex joins, nested/event-shaped data, and can sanity-check results).
You have experience with user analytics tooling (e.g., Mixpanel or Amplitude), including dashboarding/reporting and partnering with Engineering on instrumentation.
You have experience defining core SaaS metrics and building durable, trusted reporting
You have experience partnering with Product and Engineering to influence roadmap or execution.
You have a strong analytical toolkit
You have strong technical fluency: you’re comfortable working with event data and can collaborate with engineers on instrumentation and data modelling.
You have exceptional communication skills: you can explain findings to executives and IC engineers, and influence without authority.

Nice To Haves

Experience setting up or improving an event taxonomy and instrumentation practices.
Experience building a self-serve analytics culture (templates, office hours, documentation, metric dictionaries).
Familiarity with modern data stack concepts (warehouse + transformations + BI) and/or product analytics tools.
Experience in B2B SaaS and/or workflow products.

Responsibilities

Own the measurement foundation: establish and evolve our core product metric definitions (activation, engagement/session definitions, retention) and keep them consistent across teams.
Enable self-serve analytics: build blueprints and standards that make analytics self-serve for PMs and Engineering.
Partner with PMs and Engineering: act as a consulting/sounding-board partner—help shape hypotheses, interpret results, and drive decisions.
Improve reporting trust: build lightweight process and governance so data issues are caught early.
Metric systems: define and maintain a metric ladder that connects leading indicators to business outcomes (retention, activation, revenue).
Funnels & journeys: analyze onboarding, activation, and key workflows; identify drop-offs and highest-impact fixes.
Engagement & retention: cohort analysis, feature adoption, stickiness, and power-user behaviors.
Experimentation & evaluation: partner on experiment design, success metrics, and post-launch readouts.
Dashboards & narratives: create dashboards that drive action and write clear readouts that help teams make decisions quickly.