• Review and refactor existing data table structures, field semantics, and key/ID systems to resolve legacy issues such as “one field with multiple meanings” • Design and drive a unified data model and metric definitions; establish a company‑wide data dictionary and data standards • Partner closely with engineering to participate in data warehouse modeling and data pipeline (ETL/ELT) design, improving data quality and maintainability • Own the overall methodology and implementation path for A/B testing and other online experiments across the company • Design experiment pipelines, including traffic allocation, tracking/instrumentation strategy, data collection, data storage, and analysis workflows • Develop standardized experiment analysis frameworks and reusable templates, including core metrics, significance testing, sample size estimation, and evaluation guidelines • Deeply engage with core business lines and define problems and key metrics starting from product and business goals • Based on the unified data system, provide structured data analysis and experiment recommendations to product and business teams • Use data and experiment results to identify growth opportunities, product optimization directions, and potential risks, and clearly communicate findings to non‑technical stakeholders • Promote data naming conventions, field definitions, and tracking standards, and ensure their adoption across the company • Establish and maintain data quality monitoring mechanisms to detect and fix data issues • Document and socialize internal best practices related to data and experimentation, helping to build a strong data culture and improve overall data usage efficiency.