美团小象-数据开发工程师
社招全职2年以上食杂零售地点:北京状态:招聘
任职要求
1.计算机或相关专业本科及以上学历,2年以上大数据开发经验。 2.掌握数据仓库体系架构理论,精通数据仓库模型设计,具备数据治理实战经验。 3.掌握Hadoop、Spark、Hive、Flink等离线和实时计算流程和原理,并有实际开发经验和较强的调优能力。 4.掌握Doris、ClickHouse、Druid等至少一种OLAP引擎的原理和应用,具备结合引擎特性的模型设计和调优能力。 5.良好的团队合作精神和协调沟通能力,积极主动,认真踏实的工作态度,具有与产品、商分、前端、后端等多方密切配合的经验和意识。 6.具有良好的逻辑思维能力和抽象能力,理解并发现业务真实诉求。 具备以下条件优先 1.具有复杂业务场景数仓建模经验者优先。 2.具备零售商品、供应链领域业务知识者优先。 3.具备探索精神和好奇心,愿意探索大语言模型等新技术并结合业务场景落地者优先。
工作职责
1.参与小象离线和实时数仓开发,沉淀数据资产。 2.联合产品、商分等部门,高质高效交付业务需求。 3.深入理解生鲜自营即时零售业务,推动数据应用建设,提升业务决策质量和效率。
包括英文材料
学历+
数据仓库+
https://www.youtube.com/watch?v=9GVqKuTVANE
From Zero to Data Warehouse Hero: A Full SQL Project Walkthrough and Real Industry Experience!
https://www.youtube.com/watch?v=k4tK2ttdSDg
面向对象+
https://liaoxuefeng.com/books/java/oop/index.html
面向对象编程,英文是Object-Oriented Programming,简称OOP。
https://liaoxuefeng.com/books/python/oop/index.html
面向对象编程——Object Oriented Programming,简称OOP,是一种程序设计思想。
https://www.youtube.com/watch?v=SiBw7os-_zI
Learn the basics of object-oriented programming all in one video.
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.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
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.
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
ClickHouse+
[英文] Advanced Tutorial
https://clickhouse.com/docs/tutorial
Learn how to ingest and query data in ClickHouse using the New York City taxi example dataset.
https://www.youtube.com/watch?v=FtoWGT7kS-c
ClickHouse is an open-source column-oriented DBMS for online analytical processing that allows users to generate analytical reports using SQL queries in real-time.
https://www.youtube.com/watch?v=Rhe-kUyrFUE&list=PL0Z2YDlm0b3gcY5R_MUo4fT5bPqUQ66ep
OLAP+
https://www.youtube.com/watch?v=iw-5kFzIdgY
OLAP (for online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store.
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