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 jobsaverage 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.
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.
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.
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data engineer job description
The skills required to become a data engineer are:
- communication skills (data). You know about the need to translate technical concepts into non-technical language and understand what communication is required for internal and external stakeholders.
- data analysis and synthesis. You know how to undertake data profiling and source system analysis and can present clear insights to colleagues to support the end use of the data.
- data development process. You can design, build and test data products based on feeds from multiple systems using a range of different storage technologies and/or access methods. You know how to create repeatable and reusable products.
- data innovation. You know about opportunities for innovation with new tools and the use of data.
- data integration design. You can deliver data solutions in accordance with agreed organizational standards that ensure services are resilient, scalable and future-proof.
- data modelling. You understand the concepts and principles of data modelling and can produce, maintain and update relevant data models for specific business needs. You know how to reverse-engineer data models from a live system.
- metadata management. You can work with metadata repositories to complete complex tasks such as data and systems integration impact analysis. You know how to maintain a repository to ensure information remains accurate and up to date.
- problem resolution (data). You know about the types of problems in databases, data processes, data products and services.
- programming and build (data engineering). You can design, code, test, correct and document simple programs or scripts under the direction of others.
- technical understanding (data engineering). You understand core technical concepts related to the role and can apply them with guidance.
- testing. You can execute test scripts under supervision. You understand the role of testing and how it works.
The daily tasks involved in achieving these goals are varied. These include:
- extracting data and preparing it as part of the ETL (extract, transform, and load) processes
- converging data sets
- evaluating, parsing, and cleaning data sets
- coding and executing
- creating data stores and utilizing these for analysis
- using frameworks to serve data
It is the data engineer’s main responsibility to ensure the information made available to scientists and other stakeholders is true and usable. This also requires close collaboration with other team members including application developers, data scientists and database administrators.
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data engineer work environment
While they do work within a team, data engineers can perform their jobs on-site or remotely. The tools and datasets utilized for the job are all digital so there are no limitations to where they physically sit as long as they have secure access to their servers. Only company culture and policies dictate whether the work is performed on-site or virtually, but considering the current broad adoption of working from home, many data engineers are likely to continue to perform their duties remotely
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who are your colleagues?
It is the data engineer’s main responsibility to ensure the information made available to scientists and other stakeholders is true and usable. This also requires close collaboration with other team members including application developers, data scientists and database administrators.
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data engineer work schedule
With so many companies generating massive amounts of data and accelerating their digital operations, the need for business insights has never been greater. This is putting tremendous pressure on data teams to rapidly collect, extract and process information more expeditiously.
For data engineers, this can mean long days behind the desk as they face more projects. For generalists that work at small and mid-sized companies, they may be asked to work long hours to meet growing demands. The hours are dictated by a number of factors, including company culture, type of business, staff size and growth trajectory.
Increasingly, companies are deploying data engineers on a contingent or contract basis to meet their growing data needs. This allows some workers to take on various projects and gain valuable experience in different technologies to meet a variety of business needs. These arrangements also allow non-permanently hired data engineers to move from one client to another to gain more exposure to new challenges and opportunities. hours.
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advantages of finding a data engineer job through Randstad
Working with Randstad offers you a range of benefits:
- always a contact person you can fall back on and ask for help from
- many training opportunities
- a range of jobs in your area
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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do data engineers perform similar work as data scientists?
Not exactly. Engineers focus on making sure the information that will be used to create business insight is accurate, clean and ready for use by data scientists. These two roles may work closely together to ensure the analytical work results in information that business leaders can understand and use to achieve their business goals.
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will I be able to get a job right away as a data engineer after graduating from college?
Many employers look for candidates with at least a few years of work experience in the field, but with a shortage of data engineers right now, some are recruiting graduates that have strong programming and technology knowledge and problem-solving skills. The best way to get work as a data engineer is to acquire many of the base skills and build on them through additional certification and working on data projects.
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aren’t data engineers simply a subset of computer coders?
Coding is an essential skill that data engineers must possess, but their work is far more complex than just programming. An understanding of data architecture, databases and distributed systems is required. They must be able to identify issues with data sets, develop solutions to address them and integrate the data into the systems that will be used to analyze the numbers.
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is a master’s degree in data engineering required to advance in this field?
Not all companies require their data engineers and scientists to have a master’s, but to acquire a management-level role, it is strongly recommended. There are many strong data experts who work in the field without a postgraduate degree, leveraging their work experience and technology expertise to get ahead in their field. However, a master’s or Ph.D. offers greater understanding about theories and problem-solving. Additionally, certification in various tools and technologies can also help advance a career in this field.
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how do I find a job as a data engineer?
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