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.

The Role As an IT Engineering & Delivery Engineer (TPM) at Tesla Giga Shanghai, you will own the end-to-end lifecycle management of IT software systems—spanning development, testing, and delivery—while also steering the planning and execution of IT infrastructure construction projects. You will partner closely with global and local technical teams to ensure seamless synergy between software products, manufacturing digital systems, and IT infrastructure, driving IT operations excellence and fueling growth across all IT-enabled business functions, all while upholding Tesla’s rigorous standards for quality and efficiency. Responsibilities 1. Software System Development & Testing Leadership • Own the development roadmap, test execution, and deployment of business-critical systems, including business support platforms, IoT applications, and edge computing solutions. • Lead requirement gathering and solution design, bridging business stakeholders and development teams to translate on-site operational needs into actionable technical blueprints, alongside robust development plans and test strategies. • Oversee the full software testing lifecycle—from unit, integration, and system testing to User Acceptance Testing (UAT). Establish standardized test case repositories and defect resolution workflows to ensure software functionality, performance, and security align with Tesla’s global benchmarks. • Drive continuous software iteration and optimization, leveraging business feedback to enhance system adaptability and stability, and accelerate the factory’s digital transformation journey. 2. IT Infrastructure Construction & Cross-Functional Delivery • Lead IT infrastructure planning and deployment across factory construction, expansion, and new production line rollouts, covering network architecture, data center buildouts, server provisioning, end-user devices, physical security systems, and IoT device integration. • Coordinate cross-functional stakeholders (Engineering, Facilities, EHS, Manufacturing, and Security teams) to align IT infrastructure delivery timelines with master construction schedules, ensuring the hardware environment fully supports software deployment and operational demands. • Manage outsourced construction vendors and on-site teams, enforcing strict controls over quality, safety, and cost. Champion on-site standardization—including wiring protocols, rack layout, asset labeling, and document management best practices. • Oversee end-to-end integration and debugging between software systems and infrastructure, resolving compatibility issues to guarantee stable, post-deployment system performance. 3. Digital Project Execution & Technology Innovation • Lead the rollout of AI, IoT, and automation-driven digital projects, such as intelligent terminal application development, robot fleet management systems, computer vision algorithm deployment, and predictive maintenance platform implementation, to embed cutting-edge technology into manufacturing operations. • Collaborate with Tesla’s global IT teams to align system architecture, data security protocols, and technical standards, ensuring software and infrastructure solutions adhere to the company’s global technical framework. • Serve as the critical link between technology and business, driving manufacturing process optimization through software feature enhancements and strategic hardware resource allocation, boosting production efficiency and digital management maturity. 4. Project Governance & Process Optimization • Establish a unified project management framework covering the full lifecycle of both software (development-testing-delivery-operations) and infrastructure (planning-construction-acceptance) initiatives. Define clear milestones, budget guardrails, risk mitigation protocols, change management procedures, and documentation standards. • Proactively identify lifecycle risks—such as software development delays, hardware compatibility gaps, or cross-team collaboration bottlenecks—and design targeted mitigation plans to ensure on-time, on-budget, and quality-compliant project delivery. • Continuously refine project execution workflows, scaling Agile development, modular testing, and other lean methodologies to enhance cross-team collaboration efficiency and communication transparency. • Own the long-term lifecycle management of IT projects, supporting post-launch system maintenance, version upgrades, and infrastructure modernization to ensure sustained, reliable IT support for business operations.

• 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.