Sr. Product Analyst

Product strategy and insights

• Partner directly with product teams during planning and roadmap discussions to identify, assess, and prioritize opportunities before development begins.

• Define and maintain consistent metrics and measurement standards across dashboards and the semantic layer.

• Own measurement of product funnels, feature adoption, engagement, and retention across consumer-facing and school-facing experiences.

• Conduct in-depth analyses of user behavior and audience segments, pairing each finding with clear recommendations and actionable next steps.

• Proactively identify friction points, user drop-offs, and improvement opportunities, collaborating with Product, Design, and UX Research to combine behavioral data with qualitative insights.

• Experimentation and impact measurement

  • Lead A/B tests from design through analysis, including experiment sizing, guardrail metrics, duration, tracking setup, and interpretation of results.

  • Configure and validate experiment tracking through Google Tag Manager (GTM) and Google Analytics 4 (GA4).

  • Distinguish meaningful signals from statistical noise and communicate findings, limitations, and confidence levels clearly.

  • Contribute to experimentation standards and ensure tests are connected to meaningful business and product outcomes rather than superficial engagement metrics.

  • Measure launches that cannot be randomized using appropriate causal inference methods and clearly communicate the strength of the evidence.

  • Measurement implementation and data quality

    • Define measurement requirements and GA4 event specifications for new features before launch.

    • Build and test GTM tags and collaborate with Data Engineering on server-side tracking and data pipeline requirements.

    • Conduct post-launch data quality checks, review tracking changes made by partner teams, and identify issues related to consent, missing events, or incomplete tracking.

    • Build and maintain dashboards and self-service analytical tools, including Streamlit applications, to help product managers answer recurring questions independently.

    • Contribute to dbt models and the self-service semantic layer in partnership with Data Engineering.

    • Support new data systems and tracking changes, including consent management, reporting requirements, GA4 reconciliation, and launch measurement.

    • AI-powered analytics and automation

      • Use Claude and Claude Code daily to write and review SQL, explore datasets, draft analytical summaries, and build Streamlit applications.

      • Develop and maintain the semantic layer, Claude skills, and supporting context, including metric definitions, table guides, and experimentation standards, to improve the reliability of AI-assisted analytics.

      • Automate recurring analytical tasks such as experiment readouts, KPI monitoring, and launch reviews using AI-driven workflows.

      • Use AI tools to identify product launches that require measurement planning and help teams incorporate analytics into product development.

      • Enable product managers to use AI-assisted self-service analytics by maintaining approved skills and metric definitions, coaching stakeholders on interpreting data, and reviewing findings before they inform decisions.

      • Support the adoption of AI tools across Analytics and Product teams and help establish standards for validating AI-generated outputs.

      • Analytical rigor and stakeholder influence

        • Maintain high standards for reproducible SQL, documented analytical methods, validated results, and clear distinctions between correlation and causation.

        • Independently verify AI-generated queries, calculations, and reasoning before using or sharing analytical outputs.

        • Informally mentor analysts and review or challenge analyses produced by colleagues and product partners, including AI-assisted work.

        • Present findings and recommendations to product and company leadership, clearly explaining the business implications and recommended actions.

        • Over time, build a scalable measurement framework for assigned product areas, influence roadmap prioritization, and contribute improvements to the Analytics team's tools, methods, and processes.