Senior Data Analyst

Role overview

Analytics and insight delivery

• Own complex analytical initiatives from business-question framing and scoping through execution, presentation, and stakeholder follow-through.
• Deliver ad hoc analysis and actionable recommendations to customers, executives, and internal stakeholders.
• Advise cross-functional teams, challenge assumptions when data indicates otherwise, and translate findings into decision-relevant narratives.
• Design and support statistically valid A/B tests, including hypotheses, sample sizing, metric selection, and results analysis.

Google Analytics and BI

• Own GA4 reporting, custom dimensions and metrics, tracking-plan validation, and integration between Google Analytics and the data warehouse.
• Build and maintain Looker Explores and dashboards for business stakeholders.
• Develop documented and tested LookML, including dimensions, measures, derived tables, and Explore configurations.
• Maintain canonical metric definitions and production dashboards as warehouse structures change; expand self-service analytics across business units.

Data quality and platform collaboration

• Own accuracy and freshness of in-scope data products, investigate quality issues, and work with Analytics and Data Engineers on upstream fixes.
• Write performant SQL, including complex window functions and CTEs, and collaborate on query optimization, compute costs, and derived-table design.
• Collaborate on semantic-layer architecture and validate that data models reflect business logic.
• Partner on customer segmentation, lifetime value, and churn definitions, and surface machine-learning outputs in dashboards and narratives.

About Tillster

Requirements

• Strong SQL skills, including complex window functions and common table expressions.
• Experience designing, building, and maintaining Looker dashboards and Explores.
• Experience developing documented, tested LookML and semantic-layer models.
• Ability to design and analyze statistically valid A/B tests, including hypotheses, sample sizes, and success and guardrail metrics.
• Experience with Google Analytics reporting, custom dimensions and metrics, tracking validation, and warehouse integration.
• Ability to translate complex analysis into clear, actionable recommendations for stakeholders.
• Experience identifying data-quality issues and collaborating with data engineering teams to resolve them.