Contract jobs in Canada

Explore contract jobs across Canada. Choose a location and read the full job details.

18,921 listings · 4,859 employers Counted Oct 26, 2026. How we count

About these jobs

Employers hiring include Walmart Canada (957 listings), Insight Global (324 listings), Actalent (200 listings).

Common roles: Project manager, Childcare provider, Software engineer.

5,437 of these listings include employer-provided pay. Open a job to see its currency and pay period.

Advertised starting pay
Pay period	Median starting pay	Listings
CAD / hour	30.00	4,322
CAD / year	70,000.00	768
USD / hour	45.00	160
USD / year	60,000.00	40
CAD / month	4,500.00	15

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Workplace classifications: 2% remote, 24% hybrid, 73% on-site, 0% not specified. Hybrid and on-site classifications may be inferred from descriptions. Check the original ad.

Based on the dated count above. Listings can change. How we count

Contract jobs in Canada

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Swoon

Intermediate Data Engineer

Toronto, ON, Canada

$80 to $90 CAD / hour · Contract

Apply About employer

Applications open in a new tab.

Job details

Pay: $80 to $90 CAD / hour
Job type: Contract
Apply by: Nov 25, 2026
Experience: Approximately 4+ years of experience in data engineering, data integration, or a related technical role.
Posting language: English
Posted: Oct 26, 2026

Original job description

Location: Toronto, ON

Work model: 4 days onsite

Type: Contract

Duration: 12 months

Rate: 80-90/HR INC

About the Opportunity

We are seeking a Data Engineer with experience in data engineering, cloud technologies, and machine learning operations to support the development and improvement of data capabilities.

In this role, you will build, maintain, and enhance data pipelines that support analytics, machine learning, and AI applications. You will work with data scientists, technology teams, and business stakeholders to connect data sources, improve data quality, and enable reliable delivery of data to analytical models and downstream systems.

The ideal candidate is a hands-on engineer who can work through complex technical challenges, understand business requirements, and translate them into practical, scalable solutions.

Key Responsibilities

Design, develop, and maintain data pipelines that support analytics, machine learning, and AI applications.
Build and optimize data integrations, transformations, and preprocessing workflows across multiple source systems.
Develop and maintain solutions using Python, SQL, and cloud-based data engineering tools.
Support the deployment, integration, monitoring, and ongoing improvement of machine learning models and associated pipelines.
Contribute to the migration and modernization of data processes and platforms into cloud-based environments.
Prepare and structure data for predictive models, advanced analytics, and AI applications, including large language model use cases.
Troubleshoot pipeline issues, improve data reliability, and identify opportunities to enhance performance and efficiency.
Collaborate with data scientists, developers, and business stakeholders to understand requirements and deliver effective technical solutions.
Translate business needs into clear technical requirements and actionable development plans.
Document processes, technical solutions, and data workflows to support maintainability and knowledge sharing.

Required Qualifications

Approximately 4+ years of experience in data engineering, data integration, or a related technical role.
Strong programming experience with Python.
Hands-on experience with SQL and data transformation.
Experience with cloud-based data platforms and services; AWS is preferred.
Familiarity with PySpark or other distributed data processing technologies.
Experience building and maintaining data pipelines for analytics, machine learning, or AI applications.
Understanding of machine learning operations, including model deployment, integration, monitoring, or production support.
Experience working with multiple data sources and transforming complex datasets into usable formats.
Strong analytical and problem-solving skills, with the ability to break complex requirements into manageable steps.
Excellent communication skills and the ability to collaborate across technical and business teams.

Nice to Have

Experience with SAS or supporting the migration of legacy data processes to Python.
Exposure to machine learning platforms, AI integrations, or large language model workflows.
Experience in financial services or another complex, regulated environment.
Familiarity with data governance, data quality, and production data management practices.

What Makes You a Great Fit

You are a practical, solutions-oriented engineer who enjoys building reliable data solutions and improving existing processes. You can understand a system’s current state, identify the desired outcome, and develop a logical plan to get there.

You are comfortable working with technical and non-technical stakeholders, asking the right questions, and explaining complex concepts clearly. You bring technical depth, business awareness, and a willingness to learn and contribute within a collaborative team.

Apply About employer

More about this job

Responsibilities

Design, build, maintain, and optimize data pipelines and integrations for analytics, machine learning, and AI applications, including cloud migration and data preparation for predictive models and large language model use cases. Support machine learning model deployment and monitoring, troubleshoot pipeline issues, document solutions, and collaborate with technical teams and business stakeholders to translate requirements into scalable solutions.

Requirements

Requires approximately 4 or more years of relevant experience, strong Python and SQL skills, and hands-on experience with cloud data platforms, data transformations, and analytics or machine learning pipelines. Candidates should understand machine learning operations and distributed processing, with AWS preferred; SAS, legacy process migration, regulated-industry experience, and data governance are advantageous.

Skills

Python
SQL
Data Engineering
Data Pipelines
Data Integration
Data Transformation
AWS
Cloud Data Platforms
PySpark
Distributed Data Processing
Machine Learning Operations
Model Deployment
Data Quality
Data Governance
Large Language Models
Stakeholder Collaboration

Visa sponsorship

Not detected in the job text

Keywords

Python
SQL
AWS
PySpark
Data Engineering
Data Pipelines
Data Integration
Data Transformation
Cloud Computing
Machine Learning Operations
Machine Learning
Artificial Intelligence
Large Language Models
Predictive Modeling
Advanced Analytics
Distributed Data Processing
Model Deployment
Model Monitoring
Data Quality
Data Governance
Data Migration
Legacy Systems
SAS
Production Data Management
Financial Services
Stakeholder Collaboration

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