Despite reports of an early stage slowdown in Canada’s job market, employment opportunities in the data science field are only projected to grow.
According to the World Economic Forum’s Future of Jobs Report 2023, data jobs will be some of the most in demand in the future. By 2027, the report predicts a 40 percent increase in AI and Machine Learning Specialists, with a 30 to 35 percent rise in demand for Data Scientists and Big Data Specialists.
“ChatGPT has created so much attention and almost every company wants to adapt and become AI-driven, or they have to,” says Shaohua Zhang, CEO at Toronto-based data and AI training academy WeCloudData. “This will create a vast number of opportunities for new types of data scientists–people who can leverage AI to become an ultra-efficient data scientist.”
Are Advanced Degrees Essential to Landing a Data Job?
According to Zhang, hands-on project experience is attractive to employers hiring for data jobs. From a recruitment perspective, it can sometimes even trump a masters or doctorate degree.
“If the university program doesn’t have strong hiring partnerships within the industry, an advanced degree is not going to help one get a job faster, and those with practical experience are much more likely to get a job in today’s market,” he says.
Zhang explains that, while traditionally, an advanced degree is a good indicator of how a candidate might perform on the job, “today’s technologies evolve so fast that not all university programs can keep up. Hands-on project experience becomes an important indicator. Plus, having technical skills doesn’t mean that one can solve real-world problems.”
The Highest Paying Data Jobs in Canada
Using current salary data from Indeed.com, CourseCompare has compiled a list of the top 11 highest paying data jobs in Canada.
These jobs not only offer competitive compensation, but many are either fully remote or hybrid–allowing professionals to work from the comfort of their homes. On the employer side, remote work is also advantageous for recruitment, giving hiring managers access to a global candidate pool.
Below you’ll find the primary responsibilities, skills and qualifications — along with real-world examples of day-to-day work — required to break into and find your niche within the data science field.
|Data Role||Average Annual Salary|
|Machine Learning Engineer||$150,186|
|Big Data Engineer||$128,631|
|Business Intelligence Developer||$94,253|
|Business Intelligence Analyst||$82,559|
Machine Learning Engineer Salary – $150,186
Machine Learning Engineers are responsible for developing, creating, and implementing machine learning algorithms. They contribute to the innovation of new algorithms while improving existing ones, with the primary goal to enhance model accuracy.
For those unfamiliar, machine learning is a branch of AI where computer systems can make predictions or data-driven decisions by using algorithms and statistical models that previously only humans could build.
Experts in deploying machine learning systems that process extensive datasets, Machine Learning Engineers translate this data into actionable insights and predictive algorithms for their employers. Operating at the crossroads of software engineering and data science, professionals in this cross-functional role work closely with data scientists, product managers, and software engineers.
For example, Machine Learning Engineers might be used by companies such as Amazon to create product recommendation algorithms. In this context, engineers develop models that analyze user preferences and behavior to offer tailored suggestions to individual shoppers – driving more engagement and sales.
Within the customer support industry, Machine Learning Engineers might also work on Natural Language Processing projects such as building chatbots that can respond to customer questions with intelligence and empathy.
Machine Learning Engineers typically require an educational background in computer science or data science, with expertise in machine learning algorithms and deep learning frameworks such as TensorFlow and PyTorch. They should also be proficient in Python and can bolster their education with certifications like Google’s “TensorFlow Developer Certificate,” or Microsoft’s “Certified: Azure AI Engineer Associate.”
Big Data Engineer Salary – $128,631
Big Data Engineers are responsible for optimizing and managing data infrastructure within an organization. They design and develop data pipelines for storing and collecting massive datasets, and are also responsible for the maintenance of these systems.
A large part of a Big Data Engineer’s role revolves around ensuring data security and monitoring the performance of data systems while addressing any issues that arise. They’re also tasked with creating scalable data architecture and databases, all aimed at achieving efficient data processing.
For example, Big Data Engineers might be used by companies like Uber, which relies on real-time data to optimize operations. In this context, engineers can develop systems that analyze data from riders and drivers, as well as traffic conditions to optimize routes and pricing.
Big Data Engineers are skilled in working with tools and technologies such as Apache Spark, Hadoop, and NoSQL databases, and are proficient in programming languages such as Scala, Java, and Python.
They typically have either a computer science or data science degree under their belt, and can pursue certifications like Amazon Web Services’ “AWS Certified Big Data – Specialty,” Microsoft’s “Certified: Azure Data Engineer Associate,” or Google’s “Cloud Professional Data Engineer” to boost their profile.
Data Architect Salary – $125,977
Data Architects design and maintain data infrastructure within an organization. Ensuring the company’s data systems are organized and accessible is a significant part of their role, as is data security. Architects are responsible for establishing access controls, as well as data protection and encryption protocols, to keep sensitive information secure.
This role involves assessing the effectiveness of data technologies and tools that might be useful, then implementing them to improve an organization’s ability to make data-driven decisions.
For example, Data Architects at companies such as Airbnb might work on projects related to scalable data storage. In this context, they develop and maintain data storage solutions that will accommodate the continuously growing volume of user activities and property listings.
They should be proficient in SQL for database design, ETL tools, NoSQL databases, and be well-versed in programming languages such as Python, Java, and Scala. Having familiarity in data visualization tools and machine learning frameworks is also important.
Data Scientist Salary – $124,693
Data Scientists automate problem solving through deep learning and pattern recognition, as well as communicate their findings to diverse audiences–making data visualization skills a valuable asset in their toolkit.
They also uncover trends within datasets, developing predictive algorithms and data models using machine learning to improve data quality and product offerings.
For example, Data Scientists at companies such as Netflix might work on developing content recommendation algorithms that suggest movies and shows to users based on viewing history. Or they might work on content quality analysis and examine user feedback when developing content ideas and to guide licensing plans.
Data Scientists should have a degree in computer science, statistics, data science, or mathematics, while proficiency in programming languages such as Python and R are essential. They should also have knowledge in SQL, as well as experience working with data visualization tools and libraries such as Matplotlib, Seaborn, or ggplot2 in order to effectively communicate insights.
Role-specific certifications that Data Scientists can earn include Microsoft’s “Azure Data Scientist Associate,” and SAS’ “Certified Data Scientist.”
Data Modeler Salary – $112,082
Data Modelers are responsible for designing models that determine how data is organized in a database or system, while enforcing rules and limitations to ensure data accuracy.
By working closely with database administrators and developers, Data Modelers help facilitate seamless data migration processes during system upgrades. They also create comprehensive documentation for data models, such as entity-relationship diagrams, meta-data, and data dictionaries.
For example, companies such as FedEx might use Data Modelers to develop models that optimize delivery routes to improve delivery times while reducing fuel costs. Or at a financial services company, Data Modelers might work on portfolio risk analysis projects where models are developed to assess and manage the risk associated with investment portfolios.
Data Modelers should have a degree in computer science, IT, data science, engineering, mathematics or statistics. They’ll also need a strong foundation in database management, proficiency in SQL, be able to design and normalize databases, and have familiarity in data warehousing and governance.
Data Engineer Salary – $99,149
Data Engineers are responsible for the end-to-end handling of data for an organization. They collect data from databases and applications to develop data pipelines for data intake, while also taking on data cleaning duties to ensure accuracy and reliability.
They also design and manage data storage systems such as data lakes and warehouses, and are responsible for overseeing and troubleshooting data security and privacy for their employers. Data Engineers are in charge of data quality and governance, making sure that their practices are compliant with industry regulations and standards.
For example, at companies such as Netflix, Data Engineers might work on streaming data processing projects. In this context, they’ll design data pipelines that process video streaming data and real-time user interactions with the platform–with the goal of improving content suggestions and user experience.
Data Engineers usually have a degree in computer science, software engineering, data science, and IT, and can get certified as a “Google Cloud Professional Data Engineer,” or earn the “Microsoft Certified: Azure Data Engineer Associate” credentials.
Business Intelligence Developer Salary – $94,253
Business Intelligence Developers (BI Developers) are responsible for transforming raw data into actionable, data-driven insights. They develop and maintain data warehouses and databases to support reporting, and are also in charge of data cleaning.
To help other teams and senior leadership interpret data effectively, BI developers also create data visualization dashboards and tools by using software like Tableau, QlikView, and Power BI.
This cross-functional role collaborates with finance, operations, marketing, and management departments to implement data-backed solutions. Essentially, the BI developer role helps organizations bridge the gap between data and decision making, helping businesses think more strategically.
For example, a BI developer might help retail companies such as Walmart with customer segmentation or inventory management projects. Customers are segmented for targeted marketing campaigns based on purchase history and customer data, while inventory levels are optimized by taking into account sales trends.
BI developers usually hold degrees in computer science, IT, data science, business, or finance. There are also graduate degrees in business analytics, which may be a more direct path to a BI developer role.
To complement their degree, many BI developers also earn certifications in data visualization or database management tools (like Tableau, Power BI, and SQL Server). Experience with Extract, Transform, Load (ETL) tools such as Informatica, Talend, and Apache NiFi is essential for data collection work.
Statistician Salary – $93,565
Statisticians specialize in the areas of mathematics that relate to statistics and data analysis, while applying statistical methodologies to real-world business challenges. They’re responsible for developing predictive models, while uncovering trends and patterns in data to aid in decision making.
Often partnering with researchers, scientists, or business executives, statisticians are skilled at communicating valuable data insights in a way that both technical and non-technical audiences can understand.
Statisticians design experiments to evaluate the impact design changes might have on customer satisfaction, and also develop innovative data collection methods to improve demand forecasting.
For example, e-commerce giants like Amazon might rely on a Statistician to enhance their demand forecasting abilities in order to predict sales and reduce overstock. For such a project, a Statistician would collect data such as historical sales or customer behavior, while taking into account competitor pricing and seasonal trends. Ultimately, the goal will be to cater to customer needs while reducing storage costs.
Statisticians usually have a degree in statistics, mathematics, economics, or data science. To supplement their education, Statisticians can earn a “Professional Statistician (P.Stat)” certification from the Statistical Society of Canada (SSC), or become a “Certified Analytics Professional (CAP)” with INFORMS, which is recognized in Canada and internationally.
Business Intelligence Analyst Salary – $82,559
Business Intelligence Analysts (BI Analysts) are responsible for collecting and interpreting data to help organizations make data-driven decisions. Their primary duties include systematically gathering and processing data from databases, reports, and spreadsheets.
Data visualization skills are essential to a BI Analyst’s toolkit, as they often use visualization tools and software to design reports and dashboards to support strategic design making. As a part of their role, BI Analysts also assess data quality, forecast and set performance metrics, and keep an eye out for trends, patterns, and anomalies in the data.
For example, BI Analysts at companies such as Marriott International might work on projects that focus on pricing optimization. They analyze data such as occupancy rates, market trends, as well as booking history, all to adjust room rates in real-time– ultimately maximizing company profits.
BI Analysts typically have a degree in business, computer science, IT, mathematics, or statistics, and can boost their resumes with role-specific credentials such as “Certified Business Intelligence Professional” (CBIP) with the Data Warehousing Institute, and the “Microsoft Certified: Power BI” certification. Tableau also offers certification exams for business analysis professionals, demonstrating proficiency with platform-specific data visualization and analytics.
Analysts should be well-versed in data visualization tools like Tableau, Power BI, QlikView, and Looker, and be proficient with Extract, Transform, Load (ETL) tools such as Apache NiFi, Talend, Informatica, and Microsoft SSIS.
Business Analyst Salary – $74,479
Business Analysts assess business processes and devise solutions that improve operational efficiency. Besides analyzing data, they also engage in process modeling and feasibility studies to zero in on possible areas of improvement.
Business Analysts are skilled in translating technical concepts into user-friendly language, creating business cases, and overseeing projects to ensure that they’re aligned with organizational strategy.
For example, a Business Analyst at companies such as Toyota might work on projects that simplify manufacturing processes. In this context, the analyst analyzes the workflows to identify bottlenecks, and then proposes process improvements in order to reduce waste and streamline production.
Business Analysts are likely to have a degree in business administration, economics, management, finance, IT, or computer science, and can continue their education with the Data Warehousing Institute as a “Certified Business Intelligence Professional” (CBIP) or earn a “Certification of Competency in Business Analysis” (BBCA) with the IIBA.
Data Analyst Salary – $70,676
Data Analysts are responsible for extracting and analyzing data, as well as putting together data visualizations and dashboards to communicate their insights.
They usually focus only on a specific area of data analysis, such as marketing, finance, or operational data, and systematically collect data from databases, reports, and spreadsheets. Quality assurance is a key part of the Data Analyst role, along with ensuring data integrity and accuracy.
For example, companies such as Tesla might use Data Analysts for an energy efficiency project. In this context, they analyze data from the electric cars with the goal of optimizing battery performance, energy consumption, and overall vehicle productivity.
Data Analysts usually hold a degree in statistics, mathematics, computer science, or economics, although some schools and bootcamps offer specialized programs in data science or analytics to prepare students for data jobs. Tools and technologies that Data Analysts might learn include Python, R, Tableau, Power BI, and SQL.
Additional credentials that Data Analysts can add to their profile include “Microsoft Certified: Data Analyst Associate,” or SAS’ “Certified Data Scientist” certification.
How Data Scientists Can Leverage Generative AI
According to WeCloudData’s Zhang, here are some of the most sought after AI skills, tools, and technologies that employers are looking for:
- Working with generative AI tools to explore and visualize data
- Using generative AI to plan, experiment, and test modeling ideas
- Building end-to-end data science products using a combination of SQL, Python, Cloud, and Machine Learning skills
- Having a deep understanding of various scenarios and experience debugging code in production
- Working with generative AI to quickly understand new industries and use cases, helping clients build DS prototypes quickly
- Working with low-code and no-code data science and engineering tools to run experiments
- Becoming the ultimate orchestrator of complex workflows
Some believe that AI will inevitably replace data scientists, due to generative AI tools that can already visualize data through chat and prompting, as well as write Python scripts.
However, Zhang notes that AI is unable to fully automate complex tasks such as decision making, business communications, and product requirement gathering. That’s because at this stage, generative AI still falls short in terms of its reasoning abilities when compared with humans.
By adding AI skills to their arsenal, data scientists can increase their versatility and show they can handle a broader range of data-related challenges – making themselves more marketable in a competitive job market.