汽车之家高级数据架构师
社招全职8年以上技术地点：北京状态：招聘
工作描述
任职要求 基本条件 1. 本科及以上学历，计算机、数学、软件工程等相关专业优先。 2. 有大型互联网或汽车相关行业数据中台/数据架构建设经验，至少覆盖数据仓库、画像、计算平台中的两个方向。 3. 8 年以上数据开发、数据平台或数据架构相关经验，其中 2 年以上技术团队管理或技术负责经验，有带领 10 人以上团队或虚拟团队经验者优先。 技术能力 1. 深入理解数据仓库建模方法论，熟悉维度建模、Data Vault、数据湖等，具备大规模数仓建设与架构设计经验。 2. 熟悉用户画像体系搭建方法，了解标签生产、画像服务、特征工程与算法应用场景。 3. 熟悉大数据技术生态，包括但不限于 Hadoop、Spark、Flink、Kafka、Hive、Hudi/Iceberg、Doris/ClickHouse、Airflow/DolphinScheduler 等。 4. 具备实时、离线计算平台架构设计与优化经验，对数据质量…
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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
Vault+
[英文] Tutorials | Vault
https://developer.hashicorp.com/vault/tutorials
Centrally store, access and deploy secrets
https://www.youtube.com/watch?v=klyAhaklGNU
Full HashiCorp Vault Tutorial explaining What is HashiCorp Vault, How Vault works, Vault Architecture
系统设计+
https://roadmap.sh/system-design
Everything you need to know about designing large scale systems.
https://www.youtube.com/watch?v=F2FmTdLtb_4
This complete system design tutorial covers scalability, reliability, data handling, and high-level architecture with clear explanations, real-world examples, and practical strategies.
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
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.
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.
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