Data Engineering

In the age of big data and data-driven AI, many companies start to realize the importance of establishing data engineering best practices. As a result, the demand for data engineering has been growing rapidly. 

From data modelling, real-time ingestion, to data integration, and workflow orchestration, data engineers help make sure that data flows through the pipelines in the most efficient, accurate, and scalable way so that data scientists can harvest the best value out of good quality data.

Want to become a data engineer? Read on to learn more about the role of a data engineer and how WeCloudData can help you achieve it. 

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Data Engineers

There is a huge supply and demand mismatch in the talent market. One reason for the imbalance is that modern data engineering requires new tools/skills and traditional learning environments such as universities, colleges, and bootcamps don’t keep up with the trends. Another reason is that data engineering is hard to teach!

Curriculums need to be extremely hands-on, and it requires very seasoned instructors who work in the fields to teach in the most practical way. 

Don’t fall into the trap of learning just a bunch of fancy tools. 

Data Engineers need to know how to put them to work together!


-Shaohua, CEO, WeCloudData-

Common skill sets






Source: Glassdoor

Data Pipelines

Common Data Engineering Tools

What do Data Engineers do?

There are different types of data engineers. Some focus on data ingestion and integration, some focus on building the feature tables for data scientists, and others may focus on workflow orchestration or making the big data jobs more efficient. On a typical day, a data engineer may be involved in:

  • Building new connectors to ingest data from new sources
  • Automating ETL/ELT data pipelines using dbt
  • Developing and tuning Spark jobs to process big data
  • Automating the workflow using Apache Airflow 
  • Debugging failed pipeline steps
  • Monitoring and validating the data quality
  • Preparing data input for machine learning and help data scientists automate the ML pipelines

Want to know the difference between the data engineer and data scientist roles? Compare them on our learning paths page!

Who can become a Data Engineer?

At WeCloudData, while we’ve worked with professionals from backgrounds like database, traditional ETL, software development, and data science, we’ve also helped students from non-tech background to land a DE job. The key success factors include:

  • Coding
  • Strong aptitude for problem solving
  • Strong curiosity and willingness to learn new things fast
How much do Data Engineers earn?

As a Junior Data Engineer, you can expect to be paid $70k-$90k in Canada and $90k-$120k in the U.S. Some Senior Data Engineers may get paid well over $200k.

Explore our Data Engineering career guide


Data Engineering Courses

Bootcamp Programs

This 6-month instructor-led part-time program gives you the opportunity to learn from the industry best and practice what you learn through hands-on projects. You will build awesome portfolios and receive job support for 6 months after graduation.

Data Engineering
Online Live
24 Weeks
240 Hours
Short Courses

If there are specific topics in data engineering that you want to learn and apply to your work, the short courses are the best option to up-skill yourself. 

Business Intelligence, Data Engineering, Data Science
1 month
Business Intelligence, Data Engineering, Data Science
1 month
Data Engineering
8 weeks
Data Engineering
10 weeks
Business Intelligence, Data Engineering, Data Science
6 weeks
Portfolio Courses

Already acquired data engineering skills from other bootcamps and programs but want to learn how to build real projects? WeCloudData has two options for career-driven learners:

Data Engineering
Full-time, Part-time
| 6 months
Build your project today

Special discount for bundles

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