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Business Intelligence Engineer, Forecasting & Labs Analytics – Amazon – Toronto, ON

Location: Toronto, ON | Company: Amazon

Business Intelligence Engineer, Forecasting & Labs Analytics at Amazon in Toronto, ON is a full-time role paying $76,400–$127,600 CAD annually. The position sits within Amazon’s supply chain operations, specifically on the team responsible for demand forecasting across tens of millions of products. These forecasts drive inventory ordering, placement decisions, and labour planning for hundreds of warehouses.

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Day to day, you’ll work with rich datasets and tools including AWS Redshift and Tableau, partnering with Data Engineering, Data Science, and Program teams to detect, diagnose, and correct forecast problems. You’ll also develop reporting mechanisms and surface insights that help retail customers and downstream systems make better supply chain decisions.

About the Role: Business Intelligence Engineer, Forecasting & Labs Analytics

The work spans identifying where forecast accuracy can improve, designing solutions with product managers, scientists, and engineers, and generating the metrics those partners rely on. You’ll collaborate directly with machine learning and research scientists on forecasting challenges for new businesses, and communicate findings to customers in retail and downstream systems.

Earning trust with internal and external customers is a named part of the role. You’ll build and maintain reporting mechanisms that serve both audiences, which means your outputs need to be clear and reliable for non-technical stakeholders as well as engineering partners.

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Benefits and Salary

The base salary range for this position in Toronto is $76,400–$127,600 CAD annually. Amazon states that total compensation may also include sign-on payments and restricted stock units (RSUs), with final amounts determined by experience, qualifications, and location. The benefits package includes health insurance (medical, dental, vision, prescription, basic life and AD&D), a Registered Retirement Savings Plan (RRSP), a Deferred Profit Sharing Plan (DPSP), and paid time off. Workplace accommodation or adjustment is available during the application, interview, and onboarding process for candidates who need it.

Job Details

📌 Job Type: Full-Time

🏢 Company: Amazon

📍 Location: Toronto, ON

🆔 Requisition ID: 10569450

🗓️ Date Posted: October 5, 2026

💰 Pay: $76,400–$127,600 CAD annually

Responsibilities

The role covers the full cycle from identifying forecasting problems through to communicating solutions and results. You’ll work across technical and business partners, which means the responsibilities span both analytical depth and clear stakeholder communication.

  • Identify and analyze opportunities to improve forecast accuracy by detecting, diagnosing, and correcting forecast problems
  • Partner with PMs, Science, and Engineering to design solutions to complex forecasting challenges
  • Develop innovative solutions with machine learning and research scientists to address forecasting challenges for new Amazon businesses
  • Generate metrics and insights that help retail customers and downstream supply chain systems optimize decisions
  • Build reporting mechanisms for both internal and external teams
  • Communicate and earn trust with customers in retail and downstream systems

Requirements / Skills

The posting distinguishes between basic qualifications (required) and preferred qualifications. All six items under basic qualifications must be met; the two preferred qualifications would strengthen your application. Have examples of your work with large-scale datasets and SQL-based analysis ready when you apply.

  • 3+ years of data analysis experience (required): working with Redshift, Oracle, NoSQL, or similar platforms
  • Data modeling, warehousing, and ETL pipeline development (required)
  • Data visualization (required): using Tableau, QuickSight, or similar tools
  • Complex SQL query writing (required)
  • Statistical Analysis packages (required): experience with R, SAS, or Matlab
  • Python scripting (required): used alongside SQL to pull and process data for modelling
  • AWS services (preferred): EC2, DynamoDB, S3, and Redshift
  • Data mining and ETL in a business environment (preferred): with large-scale, complex datasets

How to Apply

To apply, use the link below to reach the official Amazon job posting. Have your resume up to date before submitting, as only candidates selected for an interview will be contacted regarding hiring status.

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Job Summary & Tips for Applying

AI-generated summary and tips to help you highlight your strengths effectively.

Quick Summary & What to Highlight: The Business Intelligence Engineer, Forecasting & Labs Analytics position at Amazon in Toronto focuses on improving demand forecast accuracy across millions of products using SQL, Python, and visualization tools. A candidate should put 3+ years of experience analyzing data with Redshift or similar platforms front and centre, since that is the first listed requirement and directly tied to the core work. Complex SQL writing and ETL pipeline development should also appear prominently, as both appear in the basic qualifications and map to the day-to-day duties.

Resume & Application Tips: Mirror the posting’s own terminology: write Redshift, ETL pipelines, Tableau, and statistical analysis packages (R, SAS, Matlab) exactly as listed rather than paraphrasing them. For each tool, briefly note the scale of data you worked with. For example, “Built Redshift queries processing 50M+ rows to support weekly inventory reporting” shows both the skill and the scope Amazon is looking for. If you have AWS experience (EC2, DynamoDB, S3), include that in a skills section even if it was incidental to a past role, since it appears under preferred qualifications.

Interview Preparation: Prepare a concrete example of a time you found and corrected a data quality or forecasting problem: walk through what the anomaly was, how you diagnosed it using SQL or statistical analysis, and what the downstream impact was when it was fixed. Because the role involves earning trust with retail customers and non-technical teams, also prepare to describe how you’ve explained a complex analytical finding to a non-technical stakeholder. Worth asking: what the split looks like between building new reporting and maintaining existing pipelines, and what the typical scale of the datasets you’d be querying from day one.

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