Data Analytics Intern — Hybrid, Mentorship & Impact

Capital One; Toronto; Hybrid; CAD 45,000 - 68,000; Part time

Benefits offered by this job

Hybrid work model

Mentorship

Job summary

Capital One Canada is offering a Data Analytics Engineering Intern position for Summer 2027 at our Toronto office. You will work with cross-functional teams to develop tools and data structures, owning end-to-end problem solving from identification to deployment.

The role emphasizes data quality, reporting, and fast iteration in a hybrid setup with three days in the office. Ideal candidates are pursuing a Bachelor’s degree in a quantitative field, with experience in SQL, Python, and data

Qualifications

• Pursuing a Bachelor's degree in Mathematics, Computer Science, Engineering, or another quantitative discipline with a strong programming background.
• Familiarity with SQL and/or data transformation tools such as dbt.
• Python experience would also be an asset.
• Experience building and maintaining data pipelines.
• Ability to shape and develop requirements into practical solutions.
• Ability to clearly explain technical terms to a non-technical audience.
• Experience in a Unix/Linux environment, with Git, AWS or APIs.
• Excellent verbal and written communication skills.
• Previous intern experience.

Responsibilities

• Transform data into data structures in our data lake and Snowflake data warehouse that are leveraged for business decision making.
• Create, enhance and maintain critical data processes.
• Act as a steward for our data: ensuring it is high quality, trustworthy, well documented, and easy to use.
• Build, automate, and maintain critical business reporting and visualizations that helps deliver insights.
• Perform investigative analysis on large/diverse dataset.

Education

Bachelor's degree or higher

Tools

dbt

Snowflake

SQL

Python

Git

AWS

Job description

Capital One Canada is offering a Data Analytics Engineering Intern position for Summer 2027 at our Toronto office. You will work with cross-functional teams to develop tools and data structures, owning end-to-end problem solving from identification to deployment.

The role emphasizes data quality, reporting, and fast iteration in a hybrid setup with three days in the office. Ideal candidates are pursuing a Bachelor’s degree in a quantitative field, with experience in SQL, Python, and data