Data Engineer, Early Career – 2026 – Amazon – Toronto, ON
Location: Toronto, ON | Company: Amazon
Amazon is looking for early-career talent to join its engineering teams in Toronto, Ontario for a full-time Data Engineer role starting in November or December 2026. If you’re finishing or have recently completed a degree in Computer Science, Computer Engineering, or a related field and want to build systems that operate at global scale, this is the kind of opportunity where your work genuinely reaches millions of people.
At Amazon, data engineers work on the backbone of decision-making across the business — designing data pipelines, maintaining data warehouses, and building the analytical infrastructure that engineers, analysts, and data scientists rely on every day. It’s hands-on work from day one, with real ownership and real impact.
About the Role: Data Engineer, Early Career
In this position, you’ll be responsible for designing and maintaining distributed data collection systems, building automated ETL processes, and owning the ongoing metrics and dashboards that drive key business decisions across teams. You’ll work with both SQL and NoSQL database systems, tune queries for performance, and architect solutions for future data storage and reporting needs.
Collaboration is central to the role. You’ll work alongside Business Analysts, Data Scientists, and other internal partners to identify opportunities and solve problems — and you’ll be expected to troubleshoot, research root causes, and resolve data issues thoroughly when they arise.
Benefits and Salary
The starting salary for this Data Engineer position in Toronto is $97,100 CAD annually. Amazon also provides basic life and AD&D insurance, paid time off, and additional resources aimed at improving health and well-being.
Job Details
📌 Job Type: Full-Time
🏢 Company: Amazon
📍 Location: Toronto, ON
🆔 Requisition ID: 10559101
🗓️ Date Posted: September 24, 2026
⏱️ Schedule: Monday–Friday, up to 40 hours per week, typically 8am–5pm
💰 Pay: $97,100.00 CAD Annually
Responsibilities
As a Data Engineer at Amazon, your day-to-day work centres on building and maintaining the systems that power data-driven decisions across the organization. From designing robust data pipelines to troubleshooting production issues, each responsibility plays a direct role in keeping Amazon’s analytical infrastructure running reliably at scale.
- Design and implement automated deployment of distributed systems for collecting and processing log events from multiple sources
- Architect and operate internal data warehouses and SQL/NoSQL database systems, including data schema design
- Own and maintain ongoing metrics, reports, analyses, and dashboards used by engineers, analysts, and data scientists to drive key business decisions
- Monitor and troubleshoot operational or data issues within data pipelines, researching root causes and resolving defects thoroughly
- Drive architectural planning and implementation for future data storage, reporting, and analytic solutions
- Develop code-based automated data pipelines capable of processing millions of data points
- Optimize performance by tuning inefficient database and data warehouse queries
- Collaborate with Business Analysts, Data Scientists, and other internal partners to identify opportunities and solve problems
Requirements / Skills
Amazon is looking for early-career candidates who are curious, driven, and ready to take ownership of meaningful work. The ideal candidate has a solid foundation in computer science, practical experience with data systems and SQL, and the mindset to learn quickly in a fast-moving environment.
- Education: Currently has, or is in the process of obtaining, a Bachelor’s degree or above in Computer Science, Computer Engineering, or a related field
- Age requirement: Must be 18 years of age or older
- Data experience: Experience with data mining, data transformation, and building data pipelines or automated ETL processes
- Database knowledge: Experience with database, data warehouse, or data lake solutions
- SQL proficiency: Hands-on experience with SQL is required
- Scripting skills: Experience with one or more scripting languages such as Python or KornShell
- Preferred — AWS: Experience with Amazon Web Services is a strong asset
- Preferred — Big data: Familiarity with big data processing technologies such as Hadoop or Apache Spark, ETL architecture, and reporting/analytic tools
- Preferred — Data modelling: Knowledge of data schema design basics including normalization and relational vs. dimensional models
How to Apply
To apply, visit the official Amazon job posting using the link below. Make sure your resume is up to date and tailored to the role before submitting your application.
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Job Summary & Tips for Applying
Quick Summary & What to Highlight: This Data Engineer, Early Career role at Amazon in Toronto is perfect for candidates who excel in data pipeline development, SQL and database management, and scripting with Python or similar languages. On your resume, emphasize any experience with ETL processes, data warehousing, or cloud platforms like AWS, attention to detail, and your ability to work in a fast-paced environment. If you’ve previously worked in data engineering, analytics, or software development roles or internships, make sure to highlight specific achievements and responsibilities that align with this position.
Resume & Application Tips: Before applying, tailor your resume to match the job description. Include keywords like data pipelines, ETL, and SQL/NoSQL that appear in the posting. Quantify your achievements where possible (e.g., “built an automated pipeline processing 2M+ records daily” or “reduced query runtime by 40% through optimization”). Write a brief cover letter expressing your genuine interest in Amazon and why you’re excited about this early-career opportunity in Toronto. Double-check your application for spelling errors and ensure your contact information is current.
Interview Preparation: If selected for an interview, research Amazon‘s Leadership Principles, recent engineering initiatives, and company culture beforehand. Prepare specific examples using the STAR method (Situation, Task, Action, Result) to demonstrate your technical problem-solving and data engineering skills. Common questions may include scenarios about debugging data pipeline failures, designing a data schema, or working cross-functionally with analysts and scientists. Dress appropriately for a technology and engineering environment, arrive 10–15 minutes early, and bring copies of your resume. Prepare thoughtful questions about the role, team dynamics, and growth opportunities. After the interview, send a thank-you email within 24 hours reiterating your interest in the position.
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