阿里云阿里云智能-计算平台解决方案-大数据AI产品解决方案国际业务
社招全职5年以上云智能集团地点:北京 | 杭州 | 上海状态:招聘
工作描述
任职要求 • Education: Bachelor's degree or higher in Computer Science, Software Engineering, or a related field. • English Proficiency: Strong written and verbal English skills, capable of conducting in-depth technical exchanges and product solution discussions with international English-speaking clients. • Experience: 5+ years of experience in the Big Data & AI domain, including at least 2 years in roles such as Solution Architect, Technical Consultant, or transitioning from a core R&D position. • Distributed Computing Frameworks: Deep understanding of the principles, tuning, and applicable scenarios of core components including Hadoop, Spark, Flink, Presto/Trino, and Kafka. • Data Architecture Evolution: Profound understanding and hands-on experience with Lambda architecture, Kappa architecture, and Data Lakehouse architecture, knowledgeable about compute-storage separation architecture design and cloud-based Big Data services (e.g., EMR, MaxCompute, Redshift). • Data Storage & Format: Familiarity with object storage systems like HDFS, S3, and OSS, expertise in open table formats such as Parquet, ORC, Delta Lake, Iceberg, and Hudi. • AI Infrastructure Hardware: Solid foundation in AI Infra hardware, including heterogeneous computing server architectures, high-performance networking, GPU/AI chips and interconnects, CPU/GPU/Memory/Storage access patterns, server networking topologies, multi-level storage system architectures, and their capability boundaries. • AI Practical Skills: Hands-on capabilities in Python/Shell programming, invoking Large Language Model (LLM) SDKs, and utilizing mainstream open-source frameworks, product SDKs, or standardized services for ML/DL model inference, training, and deployment. • Data Governance & Security: Familiarity with data lineage, metadata management, data quality monitoring, and privacy-preserving computation technologies. • Business Acumen: Ability to comprehend customer business objectives (cost reduction, efficiency improvement, compli…
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包括英文材料
学历+
R+
[英文] R Tutorial
https://www.w3schools.com/r/
R is often used for statistical computing and graphical presentation to analyze and visualize data.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持,它允许使用简单的编程模型跨计算机群集分布式处理大型数据集,并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
Presto+
[英文] What is Presto?
https://prestodb.io/what-is-presto/
https://www.tutorialspoint.com/apache_presto/index.htm
Kafka+
https://developer.confluent.io/what-is-apache-kafka/
https://www.youtube.com/watch?v=CU44hKLMg7k
https://www.youtube.com/watch?v=j4bqyAMMb7o&list=PLa7VYi0yPIH0KbnJQcMv5N9iW8HkZHztH
In this Apache Kafka fundamentals course, we introduce you to the basic Apache Kafka elements and APIs, as well as the broader Kafka ecosystem.
Redshift+
https://aws.amazon.com/awstv/watch/d67f8f62aca/
This video demonstrates how to quickly set up an Amazon Redshift data warehouse and analyze data using Query Editor v2.
https://aws.amazon.com/redshift/getting-started/
Find resources to get started with Amazon Redshift, a cloud data warehouse.
HDFS+
https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware.
https://www.ibm.com/cn-zh/think/topics/hdfs
Hadoop 分布式文件系统 (HDFS) 是一种管理大型数据集的文件系统,可在商用硬件上运行。
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