苹果Senior Data Engineer
任职要求
Minimum Qualifications • 3+ years of hands-on data modeling, data engineering, and software development experience. • Strong expertise in dimensional modeling and data warehousing. • Database design and development experience with relational or NoSQL databases such as MySQL/Oracle/Postgres/Teradata. • Experience with a server-side programming environment using Linux, and familiarity with scripting languages like Python and SQL. Preferred Qualifications • Good understanding of OLTP…
工作职责
You will design and build data warehouses on cloud, to provide efficient analytical and reporting capabilities across Apple’s global and regional sales and finance teams. You will develop highly scalable data pipelines to load data from various source systems, use Apache Airflow to orchestrate, schedule and monitor the workflows. Build generic and reusable solutions meeting data warehousing design standards for complex business requirements. You will be required to understand existing solutions, fine-tune them and support them as needed. Data quality is our goal and we expect you to meet our high standards on data and software quality. We are a rapidly growing team with plenty of interesting technical and business challenges to solve.We seek a self starter, who is willing to learn fast, adapt well to changing requirements and work with cross functional teams.
Key Responsibilities 1. Design and build batch/real-time data warehouses to support overseas e-commerce growth 2. Develop efficient ETL pipelines to optimize data processing performance and ensure data quality/stability 3. Build unified data middleware layer to reduce business data development costs and improve service reusability 4. Collaborate with business teams to identify core metrics and data requirements, delivering actionable data solutions 5. Discover data insights through collaboration with business owner 6. Participate in AI-driven efficiency enhancement initiatives, collaborating on machine learning algorithm development, feature engineering, and data processing workflows
职位详情 1. 负责滴滴海外国际化广告增长的效果评估及用户行为趋势，通过深度数据洞察，挖掘关键驱动因素，推动业务增长； 2. 设计并验证流量归因模型 3. 设计并实施预算分配和竞价策略的优化模型，提升投放ROI; 4. 将业务逻辑转化为明确的数据需求，校验数据集准确性，优化数据架构； 5. 参与制定数据质量标准，执行质量检查，保障分析与建模数据可靠性。 6. 搭建数据看板，实现实时数据展示与策略决策支持; 7. 与数据科学家紧密合作，定义假设，推动业务问题转化为分析模型； 8. 与数据工程师紧密配合，确保数据稳定、可扩展，满足分析需求。 9. 主导端到端数据分析项目，从需求梳理到成果交付，联动产品、工程及营销团队。 Experienced in data analytics, prefer in growth and advertising, with a strong focus on A/B testing, predictive modeling, ETL and dashboard development. Skilled in identifying actionable insights to optimize budget allocation and campaign performance. Key areas: Statistical analysis to evaluate marketing effectiveness and user behavior trends. Design and implementation of optimization models for budget distribution and campaign bidding strategies. Dashboard creation for automated performance monitoring and strategic decision-making. Close collaboration with Data Scientists to define hypotheses and translate business questions into analytical models. 🔗 Collaboration with Data Engineers Partner with data engineers to ensure data pipelines are robust, scalable, and aligned with analytical needs. Translate business logic into clear data requirements, validate output datasets, and contribute to improving data architecture. Help define data quality standards and perform QA checks to guarantee reliable inputs for analytics and modeling. 🧩 Project Management & Stakeholder Coordination Act as a bridge between technical and non-technical teams, managing timelines, deliverables, and communication for cross-functional projects. Lead end-to-end analytics initiatives from scoping to delivery, aligning with product, engineering, and marketing teams. Define priorities, set ETAs, and ensure timely execution, often using agile tools (e.g., JIRA, Asana, Notion) to track progress.
• Collaborate with BIE,DE, PM, CSM to research, design, develop, and evaluate generative AI solutions to address Global Selling challenges. • Interact with stakeholders directly to understand their business problems, aid them in implementation of generative AI solutions, brief stkaholders and guide them on adoption patterns and paths to production • Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs. • Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance). • Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer. • Establish data standardization and governance policies to ensure consistency, accuracy, and compliance. • Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. • Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools). • Provide technical guidance and best practice on data architecture that meets both traditional reporting purpose and modern AI Agent requirements.