亚马逊Sr. Business Intelligence Engineer, Risk Controls Mgt

Data Warehouse Design, Development, Maintenance and Optimization: Architect, build and manage secure, flexible and scalable data warehouse solutions. Ensure the iterations adapt to the ever-changing global technology and new business demand. Performance Optimization: Write complex and efficient SQL queries for data manipulation, transformation, and aggregation. Optimize SQL queries to improve performance and efficiency. ETL Process Management: Design, implement, and maintain ETL (Extract, Transform, Load) processes. Ensure accurate and efficient data integration from various sources into the data warehouse and the optimal model to fit downstream usage. Data Quality and Governance: Implement data quality checks, balances, and controls to ensure the accuracy and integrity of data within the data warehouse. Co-work with Risk Control Management Team to ensure the optimal data quality and compliance. Collaboration and Support: Work closely with Global Team to ensure the data foundation is consistent with global standards and adapted to the latest technology; Co-work with other business analyst, business intelligence engineer, and other stakeholders to run the data foundation ecosystem and translate data needs to robust data solutions Continuous Improvement: Stay informed of industry trends and advancements in AWS based technologies. Recommend and implement improvements to processes and technologies to enhance data warehouse functionality and efficiency.

• Translating business questions and concerns into specific analytical questions that can be answered with available data using BI tools; produce the required data when it is not available. • Apply Statistical and Machine Learning methods to specific business problems and data. • Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions. • Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds. • Develop efficient data querying and modeling infrastructure. • Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time. • Utilizing code (SQL, Python, R, etc.) for analyzing data and building statistical models.

· Translating business questions and concerns into specific analytical questions that can be answered with available data using BI tools; produce the required data when it is not available. · Apply Statistical and Machine Learning methods to specific business problems and data. · Create global standard metrics across regions and perform benchmark analysis. · Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. · Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions. · Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds. · Develop efficient data querying and modeling infrastructure. · Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time. · Utilizing code (SQL, Python, R, etc.) for analyzing data and building statistical models.

• Define and deliver large-scale BI solutions, including data modeling, KPI standardization, and pipeline automation. • Lead migration of legacy reporting into modern AWS QuickSight and other BI platforms. • Establish and maintain data foundations for social and marketing data, ensuring completeness, accuracy, and compliance. • Conduct advanced analytics, including user behavior modeling, segmentation, and marketing campaign measurement. • Generate actionable insights and recommendations to improve seller journeys, marketing effectiveness, and AI agent performance. • Collaborate with marketing operation team, data engineers, product managers, SDEs, financial analysts, and data scientists to design metrics and guide business decisions. • Drive best practices in operational excellence, data quality management, and data governance. A day in the life You will define marketing BI strategy, build scalable data models and generate actionable insights that drive product, marketing, and AI initiatives. You will partner with data engineering, product, and science teams to ensure high-quality, compliant, and scalable data foundations. Your work will directly influence AI-powered agents, seller engagement, and business growth.