IT & data jobs in Canada

Explore it & data jobs in Canada. Choose a role or place to find work that fits.

Counted Sep 25, 2026

Hiring at a glance

View jobs

Open jobs: 2,704
Employers: 1,322

Advertised starting pay

Average starting payCAD / year: $98,582Across 258 listings
Average starting payCAD / hour: $48.47Across 91 listings
Average starting payUSD / year: $116,838Across 55 listings

Employer-advertised starting pay, not employee salaries. Currencies and pay periods stay separate.

Where work happens

Remote: 5%132 listings
Hybrid: 38%1,039 listings
On-site: 56%1,517 listings

Not specified: 1% (16 listings)

Employers hiring include RBC (55), Royal Bank of Canada (48), TD (44).

Sources & calculation notes

These figures describe the listings on this role page at the date shown, not the whole job market. Listings can change.

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.

Hybrid and on-site classifications may be inferred from descriptions. Check the original ad. How we count

IT & data jobs in Canada

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Apptoza Inc.

AWS Data Engineer

Toronto, ON, Canada

Job type: Contract
Apply by: Oct 23, 2026
Posting language: English
Posted: Sep 23, 2026

Apply About employer

Applications open in a new tab.

Original job description

AWS Data Engineer

Toronto: Hybrid 4 days a week in office

As a senior technical leader, you will collaborate with architecture, analytics, and business teams to define long-term data strategy, establish engineering best practices, and support enterprise-wide data initiatives.

Key Responsibilities

Lead the design, development, and enhancement of cloud-native data engineering frameworks on AWS.

Architect, build, and optimize end-to-end batch and streaming data pipelines for scalability, reliability, and performance.

Drive experimentation and adoption of new and emerging AWS services to improve platform capabilities and operational efficiency.

Establish data engineering standards, patterns, and best practices for data ingestion, transformation, and storage.

Partner with analytics and product teams to support reporting, analytics, and business objectives.

Own data quality, monitoring, observability, and cost optimization strategies across the data platform.

Provide technical leadership and mentorship to mid-level data engineers.

Participate in architectural reviews and contribute to enterprise-wide data strategy and decision-making.

Required Qualifications

10+ years of experience in Data Engineering or Software Engineering.

4-5 years of dedicated hands-on experience with AWS.

Proven experience designing and implementing large-scale, production-grade data platforms.

Expertise with AWS data services, including:

Amazon S3

AWS Glue

Amazon Redshift

Amazon EMR

AWS Lambda

AWS Step Functions

Amazon Kinesis

Amazon MSK

Strong programming skills in Python, Scala, or Java.

Deep experience with ETL/ELT frameworks and data pipeline orchestration tools, including Airflow and AWS-native orchestration services.

Strong understanding of data modeling, distributed systems, and performance tuning.

Experience with CI/CD practices and Infrastructure as Code tools such as Terraform, AWS CDK, or CloudFormation.

Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.

Preferred Qualifications

Experience evaluating and implementing new AWS services through proof-of-concept initiatives.

Exposure to real-time streaming data architectures.

Knowledge of data governance, security, and compliance within cloud environments.

Experience supporting analytics, business intelligence, or machine learning platforms.

Experience working within Agile frameworks.

AWS certifications such as:

AWS Certified Data Analytics – Specialty

AWS Certified Solutions Architect

Technical Leadership Expectations

Serve as a senior technical leader for AWS data engineering initiatives.

Influence data platform architecture, engineering standards, and best practices.

Guide teams in building scalable, reliable, and cost-effective data solutions.

Promote innovation through the evaluation and adoption of emerging AWS capabilities.

Main Improvements

Improved structure and readability.

Eliminated repetitive language while preserving all original requirements.

Clarified leadership expectations and AWS-specific responsibilities.

Standardized qualification and technology sections for a more market-ready

Thanks & regards,

Lakshmi Bhavani

Apptoza Inc.

Phone: 678-392-3978 Ext 4640

Phone: 437-291-3224 Ext 1105

Mail id : Lakshmi.bhavani@apptoza.com

Apply About employer

More about this job

Responsibilities

Lead the design and optimization of scalable AWS data platforms and batch and streaming pipelines, while establishing engineering standards and improving data quality, observability, and cost efficiency. Provide technical leadership and mentorship, collaborate with analytics and business teams, and contribute to architecture reviews and enterprise data strategy.

Requirements

Requires 10+ years of data or software engineering experience, including 4–5 years of hands-on AWS experience and a track record building large-scale production data platforms. Candidates should have strong programming, ETL/ELT, orchestration, data modeling, distributed systems, performance tuning, CI/CD, and Infrastructure as Code experience, along with strong communication skills.

Skills

AWS Data Engineering
Data Pipeline Architecture
Batch Processing
Streaming Data
Python
Scala
Java
AWS Glue
Amazon Redshift
Amazon EMR
AWS Lambda
AWS Step Functions
Amazon Kinesis
Amazon MSK
Apache Airflow
Terraform

Visa sponsorship

Not detected in the job text

Hiring contact

Lakshmi Bhavani

Hiring contact email

Lakshmi.bhavani@apptoza.com

Keywords

AWS
Amazon S3
AWS Glue
Amazon Redshift
Amazon EMR
AWS Lambda
AWS Step Functions
Amazon Kinesis
Amazon MSK
Python
Scala
Java
ETL/ELT
Apache Airflow
Terraform
AWS CDK
CloudFormation
CI/CD
Infrastructure as Code
Data Modeling
Distributed Systems
Batch Data Pipelines
Streaming Data Architectures
Data Governance
Data Quality
Observability
Cost Optimization
Agile
AWS Certified Data Analytics – Specialty

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