亚马逊Data Engineer II, ROW AOP
任职要求
基本任职资格 - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 3+ years of experience in data engineering or related roles. - Strong programming skills in languages such as Python, Java, or Scala. - Expertise in SQL and experience with both relational and NoSQL databases. - Familiarity with cloud platforms (e.g., AWS) and their services. - Knowledge of data modeling, data warehousing, and ETL design patterns. - Experience with version control systems (e.g., Git) and CI/CD pipelines . - Strong problem-solving skills and attention to detail. - Excellent communication skills and ability to work in a collaborative team environment. 优先任职资格 - Experience working in a scientific or research-oriented environment. - Familiarity with machine learning workflows and model deployment. - Experience with Infrastructure as Code (IaC) by tools such as CDK. - Experience with streaming data processing and real-time analytics. - Experience with big data technologies (e.g., Hadoop, Spark, Hive). 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
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 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
• 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.