Data Engineer Intern at PureFacts in Toronto, Ontario, CA

About the role

We are seeking a Data Engineering Intern to assist in building and maintaining the foundational data pipelines that power our analytics and applications. This role focuses on the extraction of data from various source systems (SaaS APIs, internal services) and the loading of that data into our central data environment.

This is a technical, hands-on role ideal for a student or recent graduate looking to apply their programming skills to real-world data movement and orchestration challenges.

What you'll do

• Pipeline Development: Build and maintain basic ELT/ETL pipelines to move data from external APIs and internal systems into our database.
• API Integration: Interface with RESTful and GraphQL APIs to programmatically retrieve data using standard authentication methods (OAuth2, API Keys).
• Data Transformation: Implement lightweight data cleaning and normalization during the extraction process to ensure data integrity.
• Schema Management: Assist in the design and maintenance of destination tables and schemas.
• Monitoring & Maintenance: Monitor pipeline health, identify failures, and perform basic debugging to ensure consistent data delivery.

Qualifications

• Programming: Proficiency in Python (specifically libraries like requests, pandas, and json).
• SQL: Strong ability to write and optimize SQL queries for data insertion and retrieval.
• API Literacy: Understanding of HTTP methods (GET, POST), status codes, and JSON/XML data formats.
• Databases: Experience with at least one relational database (e.g., PostgreSQL, MySQL).
• Cloud Platforms: Familiarity with cloud environments, with a strong preference for Azure.
• Version Control: Basic familiarity with Git for collaborative development.

Bonus & Preferred Skills

• Modern Data Stack: Experience or familiarity with Snowflake as a cloud data warehouse.
• Orchestration: Understanding of basic scheduling concepts (e.g., Cron jobs, Airflow etc.).

Candidate Attributes

• Logically Driven: A first-principles approach to solving technical bottlenecks.
• Detail Oriented: High attention to data accuracy and edge-case handling in code.
• Communicative: Ability to document technical processes and explain pipeline logic clearly.