IT & data jobs in Canada

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

Counted Oct 1, 2026

Hiring at a glance

View jobs

Open jobs: 2,614
Employers: 1,328

Advertised starting pay

Average starting payCAD / year: $98,763Across 253 listings
Average starting payCAD / hour: $52.02Across 88 listings
Average starting payUSD / year: $118,134Across 53 listings

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

Where work happens

Remote: 5%131 listings
Hybrid: 40%1,033 listings
On-site: 55%1,438 listings

Not specified: <1% (12 listings)

Employers hiring include TD (44), J&M Group (40), Amazon (37).

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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Added 15 days ago
Cloud Data Engineer (GCP & AWS)
Synechron
Montreal, QC · Greater Montreal Metropolitan Area
Full-time
…Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering. Our 13 FinLabs serve as hubs where we innovate and develop cutting-edge business…
Added 15 days ago
Data Architect
Synechron
Montreal, QC · Greater Montreal Metropolitan Area
Full-time
…Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering. Our 13 FinLabs serve as hubs where we innovate and develop cutting-edge business…
Added 16 days ago
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Full-time
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Calgary, AB
Full-time
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Toronto, ON
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Toronto, ON
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Canada
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Canada
Full-time
…scalable infrastructure for AI agents, as well as optimize data processing and develop automated systems for model…

Amazon

Data Engineer, Amazon Traffic Engineering

Vancouver, BC, Canada

Experience: 3+ years of data engineering experience
Posting language: English
Posted: Sep 15, 2026

Apply About employer

Applications open in a new tab.

Original job description

We are seeking an experienced Data Engineer to build and operate the Core Data Infrastructure that underpins our ML and Science initiatives for Bot Management. You will design and own production-grade data pipelines that ingest billions of events from diverse source systems and transform raw signals into ML-ready feature groups that Science and ML Platform teams depend on for training, evaluation, and inference.

This is a hands-on engineering role at the center of a fast-moving ML organization. Our Science teams build increasingly sophisticated models, each requiring different data formats, latencies, and serving patterns. You will build the pipelines and feature infrastructure that make this possible: ingesting from Trails and Non-Trails sources, transforming disparate datasets into versioned feature groups, and operating the Feature Store that serves features consistently across all model types. You will partner directly with Applied Scientists to translate model data requirements into reliable pipelines, and work across data engineering, software engineering, and science teams to deliver data that is accurate, timely, and well-governed.

Key job responsibilities
Data Pipelines & Ingestion - Build and own batch and near real-time pipelines spanning Trails (raw and aggregated) and Non-Trails sources (Clickstream, Customer Segmentations, OPS). Evolve pipelines from Cradle/POC to production-grade using AWS Glue. Implement data quality checks, drift detection, and governance frameworks.

Feature Engineering & Serving - Build versioned feature groups across multiple storage backends (S3 for tabular data, OpenSearch for embeddings). Develop production pipelines that transform raw signals into ML-ready features, and help operate the Feature Store that serves them consistently to Science teams.

Streaming & Real-Time Systems - Develop and operate Apache Flink applications and stream processing for near real-time feature computation. Build event-driven data flows leveraging Kinesis and Kafka to support low-latency bot detection signals.

Science Partnership - Partner with Applied Scientists and ML Platform engineers to define data contracts and SLAs, understand model data requirements, and ensure feature pipelines integrate cleanly with training and inference systems.

Operational Excellence - Own the reliability, monitoring, and cost efficiency of the pipelines you build. Participate in on-call, root-cause data issues, and drive improvements that reduce operational load.

About the team
Traffic Engineering's Bot Management organization protects Amazon's ecosystem by detecting and mitigating automated threats at scale. Our Core ML Data Infrastructure team is responsible for building and operating the foundational data infrastructure that powers bot detection, AI agent identification, and content exfiltration defense. We are building a unified, model-agnostic, production-grade ML platform that brings together training, evaluation, and inference pipelines into a cohesive system serving multiple model types across the organization. Basic Qualifications: - 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience building large-scale, high-throughput, 24x7 data systems
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence Preferred Qualifications: - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, BC, Vancouver - 103,300.00 - 172,600.00 CAD annually

Apply About employer

More about this job

Responsibilities

You will design and own production-grade data pipelines to transform raw signals into ML-ready feature groups for bot management. Additionally, you will partner with applied scientists to define data contracts and ensure reliable, scalable feature serving across the organization.

Requirements

Candidates must have at least 3 years of data engineering experience and proficiency in at least one modern programming language like Python, Java, or Scala. Strong knowledge of batch and streaming data architectures and professional software engineering best practices is required.

Skills

Data engineering
Data modeling
ETL pipelines
Python
Java
Scala
NodeJS
Apache Flink
Kafka
Kinesis
AWS Glue
Data warehousing
Feature engineering
Stream processing
Data governance

Benefits

Health insurance
Medical insurance
Dental insurance
Vision insurance
Prescription insurance
Basic life insurance
AD&D insurance
Deferred Profit Sharing Plan
Paid time off
Restricted stock units

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Machine Learning
AWS
ETL
Python
Java
Scala
NodeJS
Apache Flink
Kafka
Kinesis
AWS Glue
Redshift
S3
OpenSearch
Data Pipelines
Feature Store
Stream Processing
Data Governance
Bot Management
Applied Science
Data Modeling
Data Warehousing
Cloud Computing
Distributed Systems
Real-time Systems
Monitoring
Operational Excellence
Infrastructure

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