Serve as the primary data consultant for a business domain, translating stakeholder needs into prioritized data product requirements, conducting lightweight ad-hoc analysis (SQL, Looker), triaging requests, tracking business value and ROI, and enabling teams to self-serve while ensuring data quality, adoption and measurable impact.

Supports the Revenue team by creating and maintaining Looker reports, dashboards, and visualizations. Gathers, cleans, and analyzes data from multiple sources to identify operational insights, maintain data accuracy, and improve revenue performance. Enables non-technical stakeholders to use reporting tools, supports leadership performance reviews, and provides ad hoc analysis for process improvements. Uses AI tools to accelerate data cleaning, reporting, documentation, and insight summarization.

Build and maintain dbt-based ELT pipelines and scalable data models in Snowflake or similar cloud warehouses. Ensure data quality through testing, monitoring, documentation, lineage, and CI/CD practices. Optimize SQL queries and warehouse performance, develop Looker visualizations, and collaborate with data engineers, analysts, and business users. Champion modern data-stack adoption, with preferred experience in Python, semantic layers, Airflow, and software engineering practices for data.