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 and OLAP. • Hands-on experience with Big-Data platforms like Kafka, Spark, Flink, Hive, etc. • Strong expertise in data service on Public Cloud (such as AWS, Ali Cloud, Tencent Cloud, etc.) • Evaluate business needs and conduct complex data computation, analysis, and report on results. • Ability to work individually or as part of a team. • Ability to learn quickly in a fast-paced environment. • Excellent oral and written communication, problem-solving, and critical thinking skills.
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.
• 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.
• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.
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
• 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