数据工程工程师学习路线图
数据工程岗位要求
Skill Sets required:
- Hands on experience enabling data via Adobe Analytics and/or Google Analytics
- Understanding of how customer level data is captured and stitched with behavioural data
- Experience working with Testing (QA) and Development teams, help them understand the tagging spec; able to guide as needed
- Experience working within an environment that uses tag management tools e.g. Tealium/ GTM/ ATM
- Excellent problem solving abilities
*Good to have:*
- Experience in enabling analytics tagging for mobile apps
- Programming and web development with HTML, SQL, CSS and JavaScript/jQuery
- Knowledge of Digital Marketing / online acquisition channels and attribution
- Scripting and automation withPython, R, Google Scripts etc
- Super high attention to detail as you will be responsible for ensuring 100% data accuracy
*What you will be doing:*
- Be accountable for the integrity of data collection for both behavioural and customer level data
- Gathering requirements from stakeholder groups and creating tagging spec/data layer specifications
- Ensure testing team validates data flow and participate in UAT process to provide signoff
- Build QA and production reports within Adobe Analytics or other visualisation tools to allow product teams monitor tagged deployment status and performance
- Build strong working relationships with multiple teams (Analytics, Tagging, Testing, Developers, Product teams)
*What you will bring to the role:*
- Strong understanding of digital analytics space includingweb analytics and clickstream data
- Strong troubleshooting abilities for data capture and digital analytics implementation at a granular level
- Able to work independently with guidance from remote teams
- Excellent communication skills. Be able to understand the background of the audience and be able to communicate the message in an effective manner
数据工程师学习内容
- Foundational data warehousing concepts and fundamentals
- The symbiotic relationship between data warehousing and business intelligence
- How data warehousing co-exists with data lakes and data virtualization
- Your many architectural alternatives, from highly centralized approaches to numerous multi-component alternatives
- The fundamentals of dimensional analysis and modeling
- The key relational database capabilities that you will put to work to build your dimensional data models
- Different alternatives for handling changing data history within your environment, and how to decide which approaches to apply in various situations
- How to organize and design your Extraction, Transformation, and Loading (ETL) capabilities to keep your data warehouse up to date
数据工程技术栈
补充：python/维度建模数仓/kafka/tdd/ETL工具/data pipeline/数据迁移、设计迁移、代码迁移/数据抓取/ftp获取文件数据解析入数仓
