Senior Data Analyst

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