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Analytics Engineer Jobs in Ontario (NOW HIRING)

Morson Talent

25-026 DevOps Engineer

Toronto, ON · Remote

CA$80 - CA$100/hr

... DevOps Engineer to support our data analytics developers in deploying, maintaining, and ... troubleshooting data pipelines within our Azure-based Data Lake environment. This role will be ...

Hibernian General Insurance

Analytics Manager

Markham, ON

CA$135K - CA$155K/yr

The Opportunity Join a collaborative team of engineers, data scientists and actuaries who use technology, analytics and data to shape pricing decisions across the business. As the insurance industry ...

Hibernian General Insurance

Analytics Manager

Toronto, ON

CA$135K - CA$155K/yr

The Opportunity Join a collaborative team of engineers, data scientists and actuaries who use technology, analytics and data to shape pricing decisions across the business. As the insurance industry ...

Td

Data Engineer III

Toronto, ON

CA$96K - CA$136K/yr

Build analytics to support budget guardrails, variance reporting, anomaly detection, remediation workflows, and optimization tracking. * Support commitment-based spend analysis, including reservation ...

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All JobsAnalytics Engineer JobsAnalytics Engineer Jobs in Ontario

Analytics Engineer information

See Ontario salary details

$62.5K

$109.1K

$178K

How much do analytics engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for analytics engineer in Ontario is $109,135.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $122,500.00 per year, depending on experience, location, and employer.

What is an analytics engineer?

An Analytics Engineer is a professional who bridges the gap between data engineering and data analysis. They are responsible for designing, building, and maintaining data models, pipelines, and analytics tools that enable organizations to make data-driven decisions. Analytics Engineers often work closely with data analysts and business stakeholders to ensure clean, reliable, and well-structured data is available for reporting and analysis. Their work typically involves using SQL, data transformation tools like dbt, and cloud data warehouses to create scalable and efficient data solutions.

How does an analytics engineer typically collaborate with data scientists and business stakeholders on projects?

Analytics Engineers play a critical bridge role between data engineering and data analysis. They work closely with data scientists to transform raw data into clean, reliable datasets that are ready for advanced analytics or modeling. At the same time, they collaborate with business stakeholders to understand reporting needs, ensuring that data models align with business goals. Regular communication and iterative feedback are key, as Analytics Engineers often gather requirements, build data pipelines, and adjust data products based on stakeholder input.

What are the key skills and qualifications needed to thrive as an analytics engineer, and why are they important?

To thrive as an Analytics Engineer, you need a strong foundation in data modeling, SQL, and analytics engineering principles, often supported by a degree in computer science, data science, or a related field. Proficiency with data transformation tools such as dbt, cloud data warehouses like Snowflake or BigQuery, and version control systems like Git is essential. Strong problem-solving skills, communication, and collaboration abilities help translate business needs into scalable data solutions and foster teamwork. These skills and qualities are crucial for ensuring data quality, building reliable analytics infrastructure, and enabling data-driven decision-making across organizations.

What is the difference between Analytics Engineer vs Data Engineer?

AspectAnalytics EngineerData Engineer
CredentialsOften requires SQL, Python, data modeling certificationsRequires similar skills, often with additional focus on infrastructure and systems
Work EnvironmentFocuses on data analysis, visualization, and reportingBuilds data pipelines, manages data infrastructure
Industry UsageCommon in analytics teams, BI, and data-driven rolesPrevalent in data engineering, data platform teams

While both roles work closely with data, Analytics Engineers primarily focus on transforming data for analysis and visualization, whereas Data Engineers build the infrastructure and pipelines that enable data access. Understanding these differences helps in choosing the right career path or job role.

Do analytics engineers make good money?

Analytics engineers typically earn competitive salaries that vary by experience, location, and industry. They often have skills in SQL, data modeling, and tools like Python or Spark, which can contribute to higher compensation. Overall, the role is considered well-paying within the data and analytics field.

What do analytics engineers do?

Analytics engineers design, build, and maintain data pipelines and infrastructure to enable data analysis and reporting. They work with tools like SQL, Python, and data warehouses to ensure data is accurate, accessible, and well-structured for business insights.

What are the most commonly searched types of Analytics Engineer jobs in Ontario?

The most popular types of Analytics Engineer jobs in Ontario are:

What are popular job titles related to Analytics Engineer jobs in Ontario?

For Analytics Engineer jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Analytics Engineer jobs in Ontario look for?

The top searched job categories for Analytics Engineer jobs in Ontario are:

Analytics Engineer jobs near you
Infographic showing various Analytics Engineer job openings in Ontario as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $109,135 per year, or $52.5 per hour.

25-026 DevOps Engineer

Morson Talent

Toronto, ON • Remote

CA$80 - CA$100/hr

Full-time

Re-posted 29 days ago

Key responsibilities

  • Design, implement, and maintain CI/CD pipelines for deploying data pipelines and analytics models into UAT and Production environments.

  • Support the data development team by automating code deployments, troubleshooting pipeline failures, and managing data lake infrastructure.

  • Establish and enforce DevOps standards, develop automated testing strategies, and optimize cloud-based data lake environments for efficient data processing.

Job description

Job Description Number of Vacancies: 1 Level: MP4 Hourly Rate: $80 - 100/hour Duration: 10 Months Hours of work: 35 Location: 700 University Avenue, Toronto (100% Remote) Job Overview We are seeking a skilled DevOps Engineer to support our data analytics developers in deploying, maintaining, and troubleshooting data pipelines within our Azure-based Data Lake environment. This role will be responsible for managing Cl/CD pipelines, ensuring seamless code deployment from development to UAT and production, and establishing best practices for DevOps in our data engineering and analytics functions, specifically, we are standing up a Centre of Advanced Analytics and need dedicated expertise and support. The ideal candidate will bring expertise in cloud-based DevOps, data pipeline automation, and infrastructure management, enabling our team to focus on delivering high-quality data products efficiently.

Key Responsibilities: Deployment & Environment Management Design, implement, and maintain Cl/CD pipelines for deploying data pipelines and analytics models into UAT and Production environments. Support the data development team by automating code deployments, reducing manual errors, and improving deployment efficiency. Troubleshoot and resolve pipeline failures, deployment issues, and infrastructure bottlenecks in collaboration with data developers.

Manage and optimize data lake infrastructure, security, and access controls to ensure smooth operations. DevOps Best Practices & Standardization Establish and enforce DevOps standards, best practices, and documentation to improve efficiency and reliability in data product development. Develop automated testing strategies for data pipelines to validate transformations, integrity, and performance across environments.

Work with cross-functional teams to implement observability and monitoring solutions for data workflows and deployments. Enhance version control practices and facilitate collaboration using Git, Azure DevOps, or similar tools. Infrastructure & Performance Optimization Maintain and optimize cloud-based data lake environments (Azure, Databricks, Synapse) for efficient data processing and analytics.

Automate infrastructure provisioning and configuration using Infrastructure as Code (laC) (Terraform, ARM Templates, etc.). Identify and resolve performance bottlenecks in data processing pipelines. Assist in defining and implementing security, access management, and governance policies for data and analytics environments

Collaboration & Stakeholder Engagement Act as a liaison between the Data Analytics team and the Data Lake Engineering team to ensure smooth deployments. Work closely with Data Developers, Data Engineers, and Analytics teams to troubleshoot and optimize workflows. Provide guidance and mentorship to team members on DevOps principles, automation, and best practices.

Qualifications 3+ years of experience in DevOps, Cloud Engineering, or Data Engineering with a strong focus on Cl/CD and automation. Additional MLOps experience a nice to have. Strong expertise in Cl/CD tools such as Azure DevOps, GitHub Actions, Jenkins, or GitLab Cl/CD.

Experience working with Azure-based data platforms such as Azure Data Lake, Azure Synapse, Azure Databricks. Proficiency in scripting and automation Understanding of monitoring, logging, and alerting solutions for data pipelines (Azure Monitor). Knowledge of security, access management, and compliance standards for data environments.

Strong problem-solving skills and the ability to debug complex deployment and pipeline issues. Ability to work collaboratively Experience with Databricks workflow automation, Delta Lake, Azure

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