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Added 13 mins ago
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Amazon

Data Engineer, Early Career - 2027 (CAN)

Toronto, ON, Canada

Posting language: English
Posted: Sep 24, 2026

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Original job description

NOTE: By applying to this position, your application will be considered for Toronto, ON to start in November or December 2026.

Shape what comes next | Discover careers in Tech

At Amazon, our tech isn't just a tool, it's your playground. Our engineers work on scalable systems, cloud services, and customer-facing products that operate at global scale. This is an environment where you learn by building. Whether you're creating cloud-native solutions, optimizing machine learning models, or building products that drive experiences for millions of customers around the globe, your work reaches the world, fast. You'll take ownership early, collaborate across teams and disciplines, and grow your skills as you tackle new problems. There's no single path forward here, with a chance to explore different directions and shape your journey as you go. This is where ambition meets opportunity and, where the impact of what you build helps define what comes next, for customers and for you.

What's in it for you? A career at Amazon means real responsibility from day one. You'll build, test, and learn alongside people who want to see you succeed, while making an impact that reaches far beyond campus.

This is a full-time position, meaning candidates should expect to work in office, Monday-Friday, up to 40 hours per week typically between 8am-5pm. Specific team norms around working hours will be communicated by your manager.

Key job responsibilities
As a Data Engineer, you will/may:

• Design, implement, and automate deployment of our distributed system for collecting and processing log events from multiple sources.
• Design data schema and operate internal data warehouses and SQL/NoSQL database systems.
• Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that engineers, analysts, and data scientists use to drive key business decisions.
• Monitor and troubleshoot operational or data issues in the data pipelines.
• Drive architectural plans and implementation for future data storage, reporting, and analytic solutions.
• Develop code based automated data pipelines able to process millions of data points.
• Improve database and data warehouse performance by tuning inefficient queries.
• Work collaboratively with Business Analysts, Data Scientists, and other internal partners to identify opportunities/problems.
• Provide assistance with troubleshooting, researching the root cause, and thoroughly resolving defects in the event of a problem.

A day in the life
We’re on the lookout for the curious, those who think big and want to define the world of tomorrow. Every day will be filled with exciting new challenges, developing new skills, and achieving personal growth.
How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Basic Qualifications: - Currently has, or is in the process of obtaining, a Bachelor's degree or above in Computer Science, Computer Engineering, or related fields
- Are 18 years of age or older
- Experience with data mining and data transformation
- Experience with database, data warehouse or data lake solutions
- Experience building data pipelines or automated ETL processes
- Experience with SQL
- Experience with one or more scripting language (e.g., Python, KornShell)
- Work Monday-Friday, up to 40 hours per week Preferred Qualifications: - Experience with AWS
- Experience from previous technical internship(s) or demonstrated project experience
- Knowledge of basics of designing and implementing a data schema like normalization, relational model vs dimensional model
- Experience with big data processing technology (e.g., Hadoop or ApacheSpark), data warehouse technical architecture, infrastructure components, ETL, and reporting/analytic tools and environments
- Can articulate the basic differences between datatypes (e.g. JSON/NoSQL, relational)

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 starting pay for this position is listed below. In addition, Amazon offers basic life & AD&D insurance, 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.

Toronto, ON, CAN - 97,100.00 CAD Annually

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More about this job

Responsibilities

Design, build, and maintain automated data pipelines, distributed data-processing systems, databases, data warehouses, metrics, reports, and dashboards. Monitor and troubleshoot data operations, optimize query and warehouse performance, and collaborate with analysts, data scientists, and other partners to develop data solutions.

Requirements

Applicants must be pursuing or hold a bachelor's degree or higher in computer science, computer engineering, or a related field, and be at least 18 years old. Required experience includes data mining and transformation, database or warehouse or lake solutions, data pipelines or automated ETL, SQL, and a scripting language; AWS, internships or projects, data modeling, and big-data technologies are preferred.

Skills

Data Mining
Data Transformation
Data Pipelines
ETL
SQL
Python
KornShell
Database Management
Data Warehousing
Data Lakes
Data Schema Design
AWS
Hadoop
Apache Spark
Query Optimization
Reporting and Analytics

Benefits

Basic Life Insurance
Accidental Death and Dismemberment Insurance
Paid Time Off

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Distributed Systems
Log Events
Data Processing
Data Mining
Data Transformation
Data Pipelines
ETL
SQL
Python
KornShell
Databases
Data Warehouses
Data Lakes
Data Schema
Normalization
Relational Model
Dimensional Model
AWS
Hadoop
Apache Spark
Big Data
Query Optimization
Metrics
Dashboards
Reporting
Analytics
Data Storage
JSON
NoSQL

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