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Analytics Engineer Jobs in Ontario (NOW HIRING)

Partner with Professional Services to extract, transform, and analyze client data to optimize ... Collaborate with Product, Engineering, and Customer Success to deliver high-quality, actionable ...

Collaborate with Technology, Data Governance, and Engineering teams to enhance data accessibility and analytics capabilities. Drive innovation through advanced analytics, automation, predictive ...

Primary Responsibilities The primary duty of the CFD Engineer / Analyst is to aid in modelling efforts using computational fluid dynamics (CFD) software that will optimize building designs, deliver ...

Data Analyst Engineer

Dublin, ON · Hybrid

CA$75K - CA$120K/yr

We are seeking a skilled Data Engineer to join our dynamic Data & Analytics team, within the Platforms & Transformation group. The Data Engineer will use their technical, analytical and insurance ...

New

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep ... We are seeking an experienced Data Engineer to join our team, specifically focused on building ...

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their ... We are looking for a highly skilled AI Engineer with 7+ years of experience in software engineering ...

Showing results 21-40

Analytics Engineer information

See Ontario salary details

$62.5K

$109.1K

$178K

How much do analytics engineer jobs pay per year?

As of Aug 24, 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:

Infographic showing various Analytics Engineer job openings in Ontario as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $109,135 per year, or $52.5 per hour.

Senior Solutions Engineer

Forma.ai

Toronto, ON

Full-time

Re-posted 4 days ago


Job description

The Role

As a Senior Solutions Engineer, you will play a critical role at the intersection of data, technology, and customer impact. You will lead the design and delivery of scalable, reliable, and high-impact technical implementations for our enterprise clients.

Working with diverse and complex datasets across industries, you'll build and optimize end-to-end analytics solutions that directly influence how customers engage with the Forma platform. You'll partner cross-functionally to identify opportunities, scope high-value solutions, and deliver outcomes that drive measurable business impact.

This is a highly visible, client-facing role that combines hands-on technical expertise with strategic thinking.


What You'll Do

  • Lead strategic technical engagements with enterprise clients, guiding solution design for complex business challenges using the Forma platform
  • Design, build, and optimize scalable data architectures, pipelines, and workflows (e.g., PySpark, Databricks)
  • Translate business requirements into robust, production-ready data solutions
  • Design and deliver custom code and customer-specific integrations to extend the Forma platform for unique client requirements
  • Partner with Professional Services to extract, transform, and analyze client data to optimize incentive compensation and sales performance strategies
  • Collaborate with Product, Engineering, and Customer Success to deliver high-quality, actionable datasets and insights
  • Drive improvements in data infrastructure, performance, scalability, and reliability
  • Identify opportunities to standardize and productize repeatable solutions
  • Mentor and support junior team members; contribute to internal best practices and knowledge sharing
  • Act as a trusted technical advisor to both internal and external stakeholders


What We're Looking For:

Required

  • 5+ years of experience in a technical role such as Solutions Engineer, Analytics Engineer, Data Engineer, or Sales Engineer
  • Strong proficiency in Python and SQL, with experience writing efficient, production-grade code
  • Experience with modern data lakehouse architectures (e.g., Databricks, Snowflake) and a solid understanding of performance and cost optimization
  • Experience building and maintaining scalable data pipelines and workflows
  • Strong customer empathy and a passion for delivering user-facing data products
  • Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
  • Proven ability to operate in a fast-paced, cross-functional environment and manage competing priorities
  • Strong problem-solving skills with a solution-oriented mindset
  • Demonstrated curiosity and commitment to continuous learning

Nice to Have

  • Experience with machine learning, predictive analytics, or optimization models
  • Familiarity with BI tools (e.g., Tableau, Looker, Power BI)
  • Experience with cloud platforms (AWS, Azure, or GCP)
  • Experience integrating with Salesforce or working with financial / sales data
  • Prior experience in ICM (Incentive Compensation Management), SPM (Sales Performance Management), or sales operations


What Success Looks Like:

After 30 Days:

  • Develop a strong understanding of Forma's platform, including client-facing features, data flows, and core use cases
  • Build working knowledge of our data architecture, repositories, and tooling (e.g., Databricks, S3, GitHub)
  • Establish relationships with immediate team members and key cross-functional partners (Product, Engineering, Customer Success)
  • Begin contributing to scoped technical tasks and internal data workflows with guidance
  • Begin building context on customer implementations, common challenges, and solution patterns

After 60 Days:

  • Participate in client engagements and internal solution discussions, building familiarity with customer needs and communication patterns
  • Independently execute well-defined technical tasks with high quality and reliability
  • Collaborate with cross-functional stakeholders to understand requirements and translate them into technical solutions
  • Begin contributing to medium-complexity data pipelines, transformations, or analyses with guidance
  • Begin identifying opportunities to improve existing implementations or workflows

After 90 Days:

  • Actively contribute to client-facing conversations, providing technical input and solution recommendations
  • Independently scope, design, and implement data solutions for customer use cases
  • Own delivery of medium-complexity solutions end-to-end with minimal guidance
  • Demonstrate strong problem-solving and sound technical judgment in ambiguous situations
  • Proactively identify and drive improvements in data workflows, performance, and scalability
  • Begin establishing credibility as a trusted technical partner to internal teams and customers

Long Term:

  • You consistently deliver high-quality, scalable solutions that drive measurable customer outcomes
  • You become a trusted advisor to clients and internal stakeholders
  • You identify opportunities to improve processes and influence product direction
  • You elevate the team through mentorship and technical leadership


Additional Job Info:

  • This position is for an existing vacancy
  • Salary range: $110K - $150K CAD