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Data Analytics Engineer Jobs in Ontario (NOW HIRING)

Faire

Senior Analytics Engineer - Marketplace

Toronto, ON

CA$171K - CA$235K/yr

About the role Are you passionate about using data to shape the future of Faire's product features ... What You Will Do The ideal candidate will possess a perfect blend of analytics and data engineering ...

Reconomy

Bachelor's degree in Data Analytics, Business, Finance, Engineering, or a related field. * 3-6 years of experience in data analytics, business intelligence or financial / operational analytics roles.

Cna

Senior Data Analytics Consultant

Toronto, ON · On-site

CA$90K - CA$105K/yr

Experience programming in a language such as VBA, R, or Python is desired. * Advanced technical ... Ability to analyze data and provide valuable insights. * Strong interpersonal, communication and ...

Royal Bank of Canada

Lead Data Engineer

Toronto, ON

Drive deep-dive analyses on customer behavior, product performance, campaign outcomes, and channel ... Act as a technical bridge between business stakeholders, engineering, and data science teams ...

Teranet

Data Analyst

Toronto, ON

... analytics use cases through the following responsibilities: Data Exploration (20%) * Collaborate across marketing, product, finance, partner solutions, data engineering and other teams to deeply ...

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All JobsData Analytics Engineer JobsData Analytics Engineer Jobs in Ontario

Data Analytics Engineer information

See Ontario salary details

$65.5K

$117.1K

$197.5K

How much do data analytics engineer jobs pay per year?

As of Jul 19, 2026, the average yearly pay for data analytics engineer in Ontario is $117,079.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,500.00 and $132,000.00 per year, depending on experience, location, and employer.

How do Data Analytics Engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

Can a data engineer make 200k?

Data engineers can earn $200,000 or more annually, especially with experience, advanced skills in cloud platforms, big data tools, and certifications. Salaries vary by location, industry, and company size, with senior roles and those in high-demand markets more likely to reach or exceed this level.

What engineers make $500,000?

Senior data analytics engineers with extensive experience, advanced skills in data modeling, machine learning, and proficiency with tools like Python, SQL, and cloud platforms can reach salaries of $500,000 or more, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership roles, and sometimes equity compensation.

What are the key skills and qualifications needed to thrive as a Data Analytics Engineer, and why are they important?

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

Is 40 too late for data science?

Data Analytics Engineers and data science professionals can successfully transition into the field at age 40 or older, as skills such as programming, statistical analysis, and experience with tools like Python or SQL are valuable regardless of age. Many employers value diverse experience and lifelong learning, and certifications or online courses can help enhance credentials at any age.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What does a data analytics engineer do?

A data analytics engineer designs, builds, and maintains data pipelines and systems to collect, process, and analyze large datasets. They use tools like SQL, Python, and cloud platforms to enable data-driven decision-making and often collaborate with data scientists and business teams to deliver actionable insights.
What are the most commonly searched types of Data Analytics Engineer jobs in Ontario? The most popular types of Data Analytics Engineer jobs in Ontario are:
What are popular job titles related to Data Analytics Engineer jobs in Ontario? For Data Analytics Engineer jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Data Analytics Engineer jobs in Ontario look for? The top searched job categories for Data Analytics Engineer jobs in Ontario are:
Data Analytics Engineer jobs near you
Infographic showing various Data Analytics Engineer job openings in Ontario as of July 2026, with employment types broken down into 1% Internship, 93% Full Time, 3% Part Time, and 3% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $117,079 per year, or $56.3 per hour.

Vice President Enterprise Data & Analytics

Gore Mutual Insurance

Toronto, ON • Hybrid

Other

Re-posted 29 days ago

Job description

The Vice President Enterprise Data & Analytics is accountable forestablishingand executing the data strategy to enable data-driven decision making, advanced analytics, and data innovation across the organization. This role ensureseffectivegovernance, architecture, and delivery of data platforms, analytics, and insights aligned with corporate priorities. 

The role exists to transform data into a strategic asset that drives underwriting performance, claims efficiency, customer experience, and operational excellence. Itleadsthe design and delivery of scalable data and AI capabilities, ensuring high-quality, trusted data while enabling advanced modeling and insights. 

Expected outcomes include modernized data infrastructure, enhanced business intelligence and analytics, improved regulatory and reporting capabilities, and measurable business value through data products and insights. 

Define and Lead Enterprise Data Strategy 

  • Develop and/or execute a multi-year data and analytics strategy aligned to business goals 
  • Establish data governance, quality, and stewardship frameworks across the organization 
  • Partner with executive leadership to prioritize data-driven initiatives and investments 

Deliver Scalable Data Engineering & Platforms 

  • Partner with IT to oversee design and implementation of modern data architecture (data lakes, warehouses, cloud platforms) 
  • Ensure reliable, secure, and efficient data pipelines and integration across core insurance systems 
  • Drive adoption of scalable tools, platforms, and engineering best practices 

Enable Advanced Analytics & Data Science 

  • Lead development of predictive models and AI solutions supporting underwriting, pricing, claims, and customer insights 
  • Establish frameworks for model governance, validation, and ethical AI practices 
  • Translate complex analytics into actionable business insights 

Business Intelligence & Data Enablement 

  • Deliver enterprise reporting, dashboards, and self-service analytics capabilities 
  • Drive data literacy and adoption across business units 
  • Ensure regulatory, financial, and operational reporting isaccurate,timely, and auditable 

Leadership & Talent Development 

  • Lead and develop a high-performing team of Directors across Data Engineering, Analytics, and Data Science 
  • Establish organizational design, capabilities, and succession plans 
  • Foster a culture of innovation, accountability, and continuous improvement 

 

Qualifications

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 
  • Ten (10) or more years of progressive experience in data engineering, analytics, or related domains, including Five (5) or more years in executive leadership roles. 
  • Proven success in leading data transformations and delivering measurable business outcomes. 
  • Deep expertise in a combination of the below:
    • Cloud platforms (Azure, AWS, GCP)
    • Data lakehouse/warehouse (e.g., Databricks, Snowflake)
    • ETL/ELT tools and orchestration frameworks
    • Programming languages (Python, SQL)
    • Data visualization (e.g., Tableau, Power BI)
  • Strong understanding of data governance, security, and compliance frameworks. 
  • Demonstrated ability to influence at all levels and communicate complex data concepts to non-technical stakeholders. 
  • Experience managing budgets, vendor contracts, and cross-functional initiatives. 

Additional helpful experience:

  • Certified Analytics Professional (CAP) 
  • Cloud certifications (AWS, Azure, GCP) 
  • Prior experience in the insurance industry

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