希音资深后台开发工程师(大数据平台)
社招全职3年以上信息技术类地点:南京状态:招聘
任职要求
1.本科以上,计算机相关专业。 2.至少精通java/scala其中一种开发语言,熟悉主流后端开发框架。 3.熟悉大数据平台常用框架(hadoop/hive/spark/hbase/flink/presto/clickhouse/kafka)原理及常用应用场景,至少有3年以上大型生产系统相关经验。 4.熟悉linux系统,熟悉常用的操作系统命令和shell脚本编写。 5.责任心强,有较强的沟通能力和团队合作精神。 6.了解docker、k8s相关技术,有大数据组件容器化经验优先。
工作职责
1.大数据基础平台、应用平台功能设计和开发。 2.负责大数据平台及组件的调研选型,部署,日常监控及问题解决。 3.参与海量数据处理方案设计,提供业务系统技术支撑。
包括英文材料
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
Scala+
后端开发+
https://www.youtube.com/watch?v=tN6oJu2DqCM&list=PLWKjhJtqVAbn21gs5UnLhCQ82f923WCgM
Learn what technologies you should learn first to become a back end web developer.
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big 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.
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
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.
HBase+
[英文] HBase Tutorial
https://www.tutorialspoint.com/hbase/index.htm
HBase is a data model that is similar to Google's big table designed to provide quick random access to huge amounts of structured data. This tutorial provides an introduction to HBase, the procedures to set up HBase on Hadoop File Systems, and ways to interact with HBase shell.
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
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
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.
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
Bash+
[英文] The Bash Guide
https://guide.bash.academy/
A quality-driven guide through the shell's many features.
https://www.youtube.com/watch?v=tK9Oc6AEnR4
Understanding how to use bash scripting will enhance your productivity by automating tasks, streamlining processes, and making your workflow more efficient.
脚本+
[英文] Scripting language
https://en.wikipedia.org/wiki/Scripting_language
https://zhuanlan.zhihu.com/p/571097954
一个脚本通常是解释执行而非编译。脚本语言通常都有简单、易学、易用的特性,目的就是希望能让程序员快速完成程序的编写工作。
Docker+
https://www.youtube.com/watch?v=GFgJkfScVNU
Master Docker in one course; learn about images and containers on Docker Hub, running multiple containers with Docker Compose, automating workflows with Docker Compose Watch, and much more. 🐳
https://www.youtube.com/watch?v=kTp5xUtcalw
Learn how to use Docker and Kubernetes in this complete hand-on course for beginners.
Kubernetes+
https://kubernetes.io/docs/tutorials/kubernetes-basics/
This tutorial provides a walkthrough of the basics of the Kubernetes cluster orchestration system.
https://kubernetes.io/zh-cn/docs/tutorials/kubernetes-basics/
本教程介绍 Kubernetes 集群编排系统的基础知识。每个模块包含关于 Kubernetes 主要特性和概念的一些背景信息,还包括一个在线教程供你学习。
https://www.youtube.com/watch?v=s_o8dwzRlu4
Hands-On Kubernetes Tutorial | Learn Kubernetes in 1 Hour - Kubernetes Course for Beginners
https://www.youtube.com/watch?v=X48VuDVv0do
Full Kubernetes Tutorial | Kubernetes Course | Hands-on course with a lot of demos
相关职位
社招信息技术类
1.承接实时/离线大数据处理流程开发,满足平台内业务数据需求。 2.对大数据服务进行性能调优,保障集群的高效与平稳运行,提升系统稳定性和可扩展性。 3.持续升级计算存储架构,更好支持业务发展。 工作
更新于 2025-09-28
社招3年以上信息技术类
1. 算法工程化支持:负责支持跨境治理和商品治理方向的算法工程化落地,包括算法服务化、模型部署、性能优化、A/B测试与监控体系搭建。 2. 系统开发与维护:基于Java与Python开发高性能、可扩展的算法平台和治理系统,保障算法稳定运行和高可用性。 3. 算法研发协同:与算法研究人员、产品经理紧密协作,推动图像理解、NLP、多模态及大模型等算法从研发到线上应用的全流程闭环。 4. 数据与质量评估:建设和维护治理效果数据采集、评估和监控体系,持续迭代优化模型与策略,驱动业务降本增效。 5. 技术方案创新:关注业界最新算法工程化与MLOps实践,推动内部平台能力升级,提升算法迭代效率。
更新于 2025-09-22