The company is looking to leverage advanced analytics, SQL expertise, and AI-driven tools to drive impact across the business, specifically in customer acquisition, retention, sales & marketing, and product development, while shaping strategic decisions at the highest level.
Requirements
- SQL (expert-level): Confidently write, optimize, and debug complex queries across large-scale datasets; serve as a SQL subject-matter expert for the team.
- dbt: Maintain scalable, production-grade data models and transformations that power downstream analytics.
- Snowflake: Leverage Snowflake and Snowflake Intelligence AI to deliver custom analytics, generate self-serve insights, and support data-driven decisions across the business.
- Python: Build advanced analytics workflows, statistical models, and automation scripts; integrate with AI-powered libraries and tools to enhance analysis speed and scale.
- AI-driven analytics: Identify and apply opportunities for AI (e.g., anomaly detection, text summarization, automated insight generation) to augment traditional analysis.
- Experimentation: Design and analyze A/B tests and other experiments to validate hypotheses and guide product development.
- Deep expertise in SQL — able to write complex queries, optimize performance, and leverage SQL best practices.
Responsibilities
- SQL (expert-level): Confidently write, optimize, and debug complex queries across large-scale datasets; serve as a SQL subject-matter expert for the team.
- dbt: Maintain scalable, production-grade data models and transformations that power downstream analytics.
- Snowflake: Leverage Snowflake and Snowflake Intelligence AI to deliver custom analytics, generate self-serve insights, and support data-driven decisions across the business.
- Python: Build advanced analytics workflows, statistical models, and automation scripts; integrate with AI-powered libraries and tools to enhance analysis speed and scale.
- AI-driven analytics: Identify and apply opportunities for AI (e.g., anomaly detection, text summarization, automated insight generation) to augment traditional analysis.
- Experimentation: Design and analyze A/B tests and other experiments to validate hypotheses and guide product development.
- Collaboration tools: Document workflows in Confluence and work with Engineering on data architecture and pipeline improvements.
Other
- Partner with senior leadership and stakeholders to guide business strategy through data-driven insights.
- Act as a trusted advisor, shaping prioritization, experimentation, and customer experience improvements.
- Present analyses and recommendations to executives in a clear, persuasive manner.
- Balance multiple initiatives with competing priorities.
- Proven ability to influence product strategy and business outcomes through analytics.
