苹果Data Engineer
任职要求
Minimum Qualifications • 3+ years of professional experience in data modeling, pipeline development, and software engineering • Proficiency in dimensional modeling and data warehouse architecture • Hands-on experience with both relational and NoSQL database systems • Solid understanding of Linux environments with scripting capabilities in Python and SQL Preferred Qualifications • Detailed understanding of OLTP and OLAP systems • Experience with big data technologies (Kafka, Spark, Flink, Hive, etc.) • Practical knowledge of cloud data services (AWS, Ali Cloud, or equivalent platforms) • Demonstrated ability to analyze business requirements and deliver data-driven solutions • Excellent written and verbal communication skills with strong problem-solving abilities • A Bachelor’s or equivalent experience or Master’s degree in Computer Science, Computer Engineering, or a related technical field—or equivalent practical experience.
工作职责
Design and build cloud-based data warehouses to deliver efficient analytical and reporting capabilities for Apple’s global and regional sales and finance teams. Develop highly scalable data pipelines to ingest and process data from multiple source systems, leveraging Apache Airflow for workflow orchestration, scheduling, and monitoring. Architect generic, reusable solutions that enforce to data warehousing best practices while addressing complex business requirements. Analyze and optimize existing systems, providing improvements and ongoing support as needed. Uphold the highest standards of data integrity and software quality, ensuring reliable and accurate outputs. We are looking for a proactive self-starter who takes initiative, learns fast, and works well across teams. Join our growing team where no two days are the same - solving tough technical challenges and business problems in a fast-paced environment.
- Work with global teams to enable reliable data for GCR business operations while following strict security compliance requirements, and build data foundation for GCR users to self-service for their use cases - Build and enhance data platforms to support end users to easily and securely access data and insights by leveraging AI, AWS services, and open source services - Collaborate with product managers and SDE team members to design and implement data products that meet business requirements and deliver measurable value - Implement robust data quality monitoring, validation frameworks, and governance practices while optimizing compute solutions for performance and cost efficiency
1. Design, develop, and maintain scalable data pipelines to support ML model development and production deployment. 2. Implement and maintain CI/CD pipelines for the data and ML solutions. 3. Collaborate with data scientists and other team members to understand data requirements and implement efficient data processing solutions. 4. Create and manage data warehouses and data lakes, ensuring proper data governance and security measures are in place. 5. Collaborate with product managers and business stakeholders to understand data needs and translate them into technical requirements. 6. Stay current with emerging technologies and best practices in data engineering, and propose innovative solutions to improve data infrastructure and processes for ML models and analytics applications. 7. Participate in code reviews and contribute to the development of best practices for data engineering within the team.
• 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 and BI solution
• Own team-specific data pipelines and products end-to-end. • Plan, execute, and maintain data engineering roadmaps, aligning with wider company initiatives. • Define what data is collected to serve our evolving business needs. • Develop pipelines to deliver new datasets, uncover insights, and improve decision-making. • Continuously improve the scalability, reliability, and performance of our data systems. • Support data analysts and other stakeholders with timely, accurate data. • Participate in on-call rotations to maintain pipeline stability.