亚马逊Sr. Business Intelligence Engineer, CNGS NBS (New Business & New Seller) Amazon Business
任职要求
基本任职资格 - Experience developing and presenting recommendations of new metrics allowing better understanding of the performance of the business - Bachelor's degree in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field - 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets - Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling - Good written, verbal, listening and communication skills in English. 优先任职资格 - Master Degree in Data Science, Analytics, Engineering, Computer Science, Math, Statistics with relevant experience. - · Experience working with data visualization tools and creating data visualization concepts, such as Tableau or equivalent. - · Hands on experience with statistical analysis, applying various machine learning techniques, predictive modeling and data mining. - · Experience of statistical analysis and tools such as SAS, R, or equivalent. - · Fluency in a scripting or computing language. (e.g. Python, C++, Java, etc.) - · Experience in e-commerce with AI technology as a plus. - · Natural curiosity and desire to learn. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
工作职责
· 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.
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.
• 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.
Every day will bring new and exciting challenges on the job while you: - Act as a strategic advisor for customers' Generative AI initiatives and internal AI agent innovation - Drive the development and implementation of collaborative AI agents within the TAM organization - Lead technical discussions around AWS AI services including Bedrock, Claude, and Amazon Q. - Make recommendations on AI architecture, security, cost optimization, and operational excellence - Champion internal AI agent success stories to inspire customer innovation - Complete analysis and present periodic reviews of AI workload performance - Guide customers in developing responsible AI practices while ensuring security and compliance - Foster an ecosystem where AI and humans progress together through knowledge sharing - Work with AWS AI/ML service teams to advocate for customer needs - Participate in customer requested meetings (onsite or via phone) - Work directly with Amazon Web Service engineers to ensure rapid resolution of AI-related issues - Available in non-business hours to handle urgent issues ------------------------------------------------
TEAM, ROLE INTRODUCTION: The Strategy, Performance Management & Intelligence (S&PMI) team is in charge of managing the Key Performance Indicators (KPI) covering service performance, contract compliance, network and operation compliance and market intelligence. We also collaborate with other functions in Platform Logistics to ensure the suitability, compliance of these KPIs. The successful candidate will be responsible for building up systematic performance management, market intelligence visibility and any strategic projects in order to steer the performance following the regional strategy direction. DUTIES & RESPONSIBILITIES 1. Strategic Performance Management & Planning ● Execute a comprehensive performance management strategy aligned with organizational goals. ● Define, track, and refine KPIs to measure business performance across departments. ● Work hand in hand with other functions in Regional and across Logistics ventures to ensure the suitability, compliance of these KPIs. ● Design and control governance, framework of working process related to critical domain in PM as regional level to drive for performance improvement. ● Measure, monitor and report Logistics Performance in terms of Delivery performance, buyer & seller experience on modes of Business as usual (BAU) and Mega Campaigns (CP). ● Identify opportunities for process optimization, cost reduction, and revenue growth through data analysis. 2. Business Intelligence ● Lead the design and implementation of advanced BI solutions (e.g., dashboards, reports, predictive models) to support real-time decision-making. ● Analyze historical and real-time data to uncover trends, root causes of performance gaps, and actionable recommendations. ● Collaborate with BI, PMI and operations teams across ventures to integrate data sources and ensure alignment with business objectives. ● Partner with internal DE, Tech teams to implement data governance and maintain data infrastructure, BI tools… 3. Stakeholder Engagement ● Present insights and recommendations to management and senior leadership, translating complex data into clear, actionable strategies. ● Act as a trusted advisor to business units, guiding them in leveraging data to achieve operational excellence. ● Facilitate sessions to align stakeholders on performance metrics and BI priorities. ● Participate in strategic project and ensure favorable milestone.