what is a data engineer?

Data engineers collect, manage and administer data. They are a critical part of any data operation by creating the architecture for acquiring and processing raw data and then preparing it for data scientists to analyze this information and create insights from it. Data engineers identify trends in data sets and develop algorithms as part of the prep work. Like many IT roles, data engineers possess very deep and specific technical skills, such as SQL database design, multiple programming languages and cloud services. 

In addition to the need for technical skills, they are part of a team that must deliver critical insights needed by business leaders to guide their day-to-day and long-term strategic goals. By enabling these executives to quickly understand and react to immediate and emerging trends, analytics teams play an important role in facilitating outcomes for their organizations.

From one day to the next, data engineers work with business and IT colleagues to develop architecture and create interfaces (APIs) that improve the usability of data. Whether they are preparing the information for use in a dashboard, to be imported into a database or extracted for other purposes, the engineer is responsible for ensuring the integrity of the data and pipelines. Other regular tasks include combining different data sets, determining how to store the information and working with data scientists and analysts to acquire the needed insights.

Data engineers typically fall into one of three types: generalists (oversees all data tasks within an organisation including analytics), pipeline-centric (manages all the data flow into the company) and database-centric (works with multiple databases). The size of the organisation often dictates the type of data engineer employed since smaller ones may be limited to a small team or even just one individual managing the data. Companies with more resources may be able to deploy more engineers to support a higher volume and broader analytical needs.

data engineer jobs

average data engineer salary

According to our Salary Guide, the salary of data engineers in Canada can vary significantly depending on several factors, including the specific location, level of experience, the size and industry of the employer, and the skills and qualifications of the individual. 

In general, data engineer salaries tend to range from $60,000 to $150,000 or more annually. Here's a breakdown by experience level:

  • entry-level data engineers (0-2 years of experience): in cities like Toronto, Vancouver, and Montreal, you can expect salaries in the range of $60,000 to $90,000 per year.
  • mid-level data engineers (3-5 years of experience): data engineers with a few years of experience can earn salaries in the range of $90,000 to $120,000 or more.
  • senior data engineers (5+ years of experience): senior data engineers with significant experience and expertise can earn salaries ranging from $120,000 to $150,000 or higher, especially in major metropolitan areas.

Salaries may also vary by location within Canada, with cities like Toronto, Vancouver, and Montreal generally offering higher salaries due to the higher cost of living. Additionally, data engineers with specialized skills in areas like big data technologies, cloud platforms, and specific programming languages may command higher salaries.

download our salary guide

types of data engineers

Some of the types of data engineers include:

  • big data engineer: focuses on managing and optimizing the storage, processing, and analysis of large volumes of data using tools like Hadoop and Spark.
  • data architect: designs the structure and organization of data systems, ensuring data is stored, accessed, and utilized efficiently and securely.
  • data analyst: analyzes data to derive insights and support decision-making, often using statistical and visualization tools.
  • machine learning engineer: develops and deploys machine learning models to solve specific business problems by leveraging data.
  • data scientist: combines statistical and computational skills to explore and extract valuable insights from data, often using advanced analytics and machine learning techniques.
  • software engineer: builds software applications and systems that incorporate data processing and analysis components.
  • data programmer: writes code to transform, clean, and manipulate data, enabling it to be used effectively in various applications.
  • data warehousing engineer: focuses on creating and maintaining data warehouses and data storage solutions for structured data.
  • cloud engineer: manages cloud infrastructure and services, ensuring scalability, security, and reliability for data-related applications and systems.
data engineers
data engineers

working as a data engineer

What is your day-to-day life like if you work as a data engineer? Here's what you can expect working in this profession.


data engineer education

To become a data engineer in Canada, you typically need a combination of education, skills, and practical experience. While there is no strict educational requirement, the following educational pathways and qualifications can help you build a strong foundation for a data engineering career:

  • bachelor's degree: many data engineers hold a bachelor's degree in a related field such as Computer Science, Computer Engineering, Software Engineering, Information Technology, or a similar discipline. These programs provide a solid foundation in computer science, programming, and database management.
  • master's degree (optional): while not always required, having a master's degree in a relevant field can be an advantage, especially if you want to specialize in a particular area of data engineering. Some data engineers pursue a Master's in Data Science, Data Engineering, or a related field to gain advanced knowledge and skills.
  • relevant courses and certifications: consider taking courses or earning certifications related to data engineering and its associated technologies. For example, courses in database management, big data technologies (e.g., Hadoop, Spark), cloud computing (e.g., AWS, Azure), and data warehousing can be beneficial. Certifications from providers like AWS, Microsoft, Google, or Cloudera can also help demonstrate your expertise.
  • programming skills: data engineers typically need strong programming skills. You should be proficient in languages like Python, Java, or Scala, as well as have experience with technologies like SQL for database management.
  • database and data warehousing knowledge: understanding databases, data modeling, ETL (Extract, Transform, Load) processes, and data warehousing concepts is crucial for data engineering. Familiarity with databases like SQL Server, Oracle, or NoSQL databases (e.g., MongoDB) is important.
  • big data technologies: data engineers often work with big data technologies like Hadoop, Spark, and distributed data storage systems. Familiarity with these technologies is an asset.
  • cloud computing: many data engineering projects are hosted on cloud platforms like AWS, Azure, or Google Cloud. Having knowledge and experience in working with cloud services can be highly valuable.
  • practical experience: internships, co-op programs, or entry-level positions in data-related roles can provide valuable hands-on experience. Building personal projects or contributing to open-source data engineering projects can also demonstrate your skills.
  • continuous learning: the field of data engineering is constantly evolving, with new technologies and tools emerging. Staying up to date with industry trends and continuously learning is essential for a successful data engineering career.

data engineer skills and competencies

Data engineers need to be well-skilled in data architecture and database design and maintenance. To competently perform their jobs, they are required to have strong knowledge of a variety of technologies and languages – as many as 10 to 30 to choose the best tools for the projects they work on. Many organisations often will deploy a single suite of cloud services from one vendor, so having a deep understanding of one platform is often necessary, whether that’s on AWS or Azure. 

Some of the skills a data engineer needs include:

  • Apache Spark
  • SQL
  • Hadoop
  • Beam
  • Java
  • Python
  • R
  • Kafka
  • Extract/Transform/Load (ETL)
  • Amazon Web Services
  • Databases
  • Shell scripting
  • Distributed ML Platforms: MLib (Spark)
  • Parallel Computing for Deep Learning (Tensorflow, GPU Programming)
  • Development in Containers (Docker, Rkt)
  • Programming in Notebooks (Zeppelin, Jupyter)
  • Java, C++, and/or Go and functional languages (Scala, Clojure, Elixir)

Beyond technical skills, career advancement also requires many soft skills typically possessed by managers in any function: strong communication, team-oriented collaboration, good project management and efficient use of time. Because data engineers are typically asked to fulfill a business need, they must be able to work with a number of data colleagues and operational leaders to determine the objective of any project or initiative.


FAQs about data engineer jobs

Here, you will find the answers to the most frequently asked questions about the profession of a data engineer.


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