Senior Product Data Analyst

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Requirements:

• Highly experienced in data analysis, preferably within a product or technology-driven environment.
• Fluent in SQL and python for data analysis.
• Skilled at constructing meaningful data visualizations.
• Comfortable with the command line, git and dbt.
• Experienced with A/B testing, experimentation, and data-driven optimization.
• Knowledgeable about database architecture, data warehousing principles, REST APIs, event tracking frameworks.
• Experienced at scoping broad, loosely-defined requests into well-structured plans.
• Highly curious, with a strong attention to detail and a perspective on how work fits into the 'big picture'.
• Adept at using excellent communication skills to translate complex analyses and findings into actionable insights for stakeholders.
• Able to work independently and efficiently with minimal oversight.
• Familiar with SaaS metrics and business practices.
• Comfortable working in agile environments and collaborating with cross-functional teams.
• Passionate about data.

Responsibilities:

• Use SQL and python to analyze complex datasets, identifying trends, patterns, and insights to inform business-critical decisions and product development.
• Design and maintain self-serve data visualizations and dashboards to provide real-time insights for stakeholders.
• Perform A/B test, cohort and funnel analyses in collaboration with Product teams to optimize user experience and business outcomes.
• Partner with product managers, engineers, and cross-functional stakeholders to define metrics, KPIs, and success criteria for product releases.
• Meet Product stakeholders 'where they live' by becoming an expert in our application data models, usage of Amplitude and internal event tracking frameworks.
• Translate data insights into clear recommendations that support product strategy and feature prioritization.
• Communicate findings and recommendations to technical and non-technical audiences through easily-interpretable presentations, reports, and dashboards.
• Work with data engineers to ensure data accuracy, availability, and integrity across product analytics.
• Use dbt to define version-controlled, modular business logic that can be easily reused across the team.
• Develop and improve data collection methods and processes to enhance the quality of product analytics.