数据工程工程师学习路线图-编程知识

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