Role Purpose The Senior Data Analyst is responsible for transforming data into clear insights that drive product, business, and operational decisions. The role bridges data, product, and business by asking the right questions, validating assumptions, and making complex information understandable and actionable. Data Analysts work closely with Product, Engineering, Marketing, and Leadership to ensure decisions are informed by reliable data. This role also includes active and responsible use of AI tools to accelerate analysis, exploration, and communication of insights - while maintaining strong analytical judgment and data integrity. Senior Data Analyst Focus: Analytical leadership and decision influence Owns analytics for a product area or business domain Shapes how success is measured and evaluated Guides stakeholders toward data-informed decisions Mentors junior and mid-level analysts Improves analytical practices, tooling, and data literacy Uses AI strategically to deepen insights and reduce time-to-decision Ability to independently manage analytical projects end-to-end Define scope, timelines, and deliverables Communicate progress and expectations to stakeholders Balance multiple priorities and requests effectively Typical experience: 5+ years of data analytics experience Core Responsibilities Analyze product, user, and business data to support decision-making Define and track key metrics, KPIs, and success indicators Build and maintain dashboards, reports, and self-service analytics Partner with Product and Engineering to evaluate experiments and changes Validate data quality and highlight inconsistencies or risks Communicate insights clearly to technical and non-technical stakeholders Use AI tools to speed up analysis, hypothesis exploration, and storytelling Core Data Analytics Expertise Data Analysis & Exploration Exploratory data analysis (EDA) Hypothesis-driven analysis Identifying trends, patterns, and anomalies Translating vague questions into analytical approaches SQL & Data Access Strong SQL skills for querying analytical databases Joining, aggregating, and transforming data Understanding data freshness, granularity, and limitations Metrics & Measurement Defining meaningful metrics and KPIs Funnel, cohort, and retention analysis Experiment and A/B test analysis Understanding bias, noise, and statistical significance (practical level) Event-based / product analytics (user behavior tracking, event design, feature usage analysis) Revenue, LTV, and unit economics analysis (CAC, payback period, ROI) Visualization & Communication Building clear dashboards and reports Data visualization best practices Presenting insights in a clear, concise narrative Adapting communication to different audiences