• Translate ambiguous business questions into structured analytical frameworks • Develop KPI definitions, metric layers, and standardized reporting logic across teams • Partner with Product and Growth teams to define success metrics and measurement strategies • Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency • Present insights and trade-offs clearly to senior stakeholders and leadership • Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines • Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference • Build experimentation dashboards and automated reporting systems • Partner with Data Science to productionize predictive models and design reusable ML-ready datasets • Support feature engineering and monitoring for production ML systems • Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases • Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics • Create trusted, well-documented datasets for product, finance, operations, and growth teams • Improve data quality, observability, lineage, and governance across the analytics ecosystem • Optimize performance across warehouse and transformation layers • Drive best practices in analytics engineering across the organization • Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind • Mentor analytics engineers and elevate modeling and measurement standards • Champion a data-driven culture grounded in experimentation and measurable impact