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Data Engineering Intern Jobs in Toronto, ON (NOW HIRING)

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Research Intern

Toronto, ON · On-site

Enter data and prepare reports and presentations summarizing research findings. * Collaborate with ... Experience with statistical software (e.g., SPSS, SAS) or programming languages (e.g., R, Python ...

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All JobsData Engineering Intern JobsData Engineering Intern Jobs in Toronto, ON

Data Engineering Intern information

See Toronto, ON salary details

$9

$42

$92

How much do data engineering intern jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for data engineering intern in Toronto, ON is $42.29, according to ZipRecruiter salary data. Most workers in this role earn between $16.75 and $67.68 per hour, depending on experience, location, and employer.

What does a data engineering intern do?

A Data Engineering Intern assists in building and maintaining the systems and infrastructure that allow organizations to collect, store, and analyze large volumes of data. Their responsibilities often include cleaning and organizing raw data, developing data pipelines, and supporting the work of data engineers and data scientists. Interns may also work with tools like SQL, Python, and cloud platforms to automate data workflows. This role provides hands-on experience in managing data processes and understanding the fundamentals of data engineering in a real-world environment.

What types of projects and technologies can a data engineering intern expect to work with during their internship?

As a Data Engineering Intern, you’ll typically work on projects involving data pipeline development, data cleaning, and integration of data from various sources. You can expect to use technologies like SQL, Python, and tools such as Apache Spark, Hadoop, or cloud platforms like AWS or Google Cloud. Interns often collaborate closely with data engineers, analysts, and sometimes data scientists to ensure data is accessible, reliable, and well-organized for analysis. This hands-on experience helps you build foundational skills and understand real-world data workflows within a collaborative team environment.

What are the key skills and qualifications needed to thrive as a data engineering intern, and why are they important?

To thrive as a Data Engineering Intern, you need foundational knowledge in programming (especially Python or Java), databases, and data structures, often obtained through coursework in computer science or related fields. Familiarity with SQL, cloud platforms (like AWS or Azure), and data pipeline tools such as Apache Spark or Airflow is typically required. Strong problem-solving skills, attention to detail, and effective communication set exceptional interns apart. These skills and qualities are crucial for efficiently handling data workflows, collaborating with teams, and contributing to high-quality data solutions.

What is the difference between Data Engineering Intern vs Data Analyst Intern?

AspectData Engineering InternData Analyst Intern
Required SkillsBasic SQL, programming (Python, Java), understanding of data pipelinesData visualization, SQL, statistical analysis
Work EnvironmentData engineering teams, cloud platforms, data warehousesBusiness intelligence teams, reporting tools, dashboards
Industry UsageTech, finance, healthcare, any data-driven industryMarketing, finance, retail, business sectors

While both roles involve working with data, a Data Engineering Intern focuses on building and maintaining data pipelines and infrastructure, whereas a Data Analyst Intern analyzes data to generate insights and reports. The roles share some technical skills like SQL but differ in their core responsibilities and work environments.

What are the most commonly searched types of Data Engineering jobs in Toronto, ON?

The most popular types of Data Engineering jobs in Toronto, ON are:

What cities near Toronto, ON are hiring for Data Engineering Intern jobs?

Cities near Toronto, ON with the most Data Engineering Intern job openings:

Data Engineering Intern jobs near you
Infographic showing various Data Engineering Intern job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution, with an average salary of $87,962 per year, or $42.3 per hour.

Applied AI Engineer (Agentic Workflows & RAG) - Master-Level Internship

Vosyn

Etobicoke, ON • On-site, Remote

$32/hr

Contractor

Re-posted 4 days ago

Job description

About Us:

At Vosyn, we embrace the exciting, game-changing world of Artificial Intelligence, driving innovation and pioneering impactful projects across various industries. We are a trailblazing Language Synthesis AI firm reshaping global communication by dissolving language barriers and empowering users. We believe in fostering a culture of flexibility, continuous improvement, and solution-focused strategies. Here, every idea is welcomed, nurtured, and has the potential to scale to new heights. Currently, we're at the forefront of a significant IPO endeavor, truly a unicorn in the making. We invite you to be part of our journey and leave your imprint on the future of AI.

About the Role:

We are seeking a sharp, technically deep Applied AI Engineer Intern to own the intelligence layer of what we build. This role is ideal for a Master's level student who does more than use AI coding tools - you understand how modern language and reasoning models actually behave, and you build with them as components. You will design and ship agentic workflows (systems where a model plans, acts, and re-plans in a loop), build retrieval-augmented generation (RAG) and search integrations that ground AI in real, current data, choose the right model for each job, and write the evaluations that prove the system works rather than just appears to. This is the role that delivers the "AI" in AI consulting: when a client has a scoped roadmap, you are the person who stands the tool up.

Tools & Tech Stack:

Agentic coding: Claude Code (primary), plus Cursor or Windsurf

AI APIs & SDKs: Anthropic Claude API and comparable model APIs; reasoning and instruction-tuned models

Retrieval & RAG: vector databases (e.g., pgvector, Pinecone, Weaviate), embeddings, semantic and hybrid search

Orchestration & integration: MCP (Model Context Protocol), function/tool calling, agent frameworks

App & data layers: React.js / Next.js, Node.js / Python, Supabase or Firebase,PostgreSQL

Evaluation: prompt and output evaluation harnesses, test sets, regression checks for non-deterministic systems

Version control & documentation: Git / GitHub, Notion

Key Responsibilities:

Design, build, and harden agentic workflows that plan and take actions reliably - and understand why agents fail (context loss, compounding errors, no feedback signal) and how to structure tasks so they succeed.

Build retrieval (RAG/search) pipelines that fetch the right client data and ground model outputs in it, integrated into core applications rather than demos.

Select the right model for each task - reasoning model vs. fast instruction model - and be able to justify the trade-off in latency, cost, and quality.

Engineer prompts and context structures appropriate to the model class, including knowing when reasoning models need framing rather than step-by-step hand-holding.

Write evaluations for AI features, because with non-deterministic models "it worked once" is not evidence that it works.

Connect AI tools to internal systems and data sources via APIs or MCP to power real client use cases.

Review and validate AI-generated code and automated workflows critically for correctness, security, and safety.

Collaborate with the Builder and the Integration & Data Engineer to deliver complete, working solutions, and document workflows, prompts, and integrations in Notion.

About You:

Currently enrolled or recently graduated from a Master's program in Computer Science, Software Engineering, AI/ML, Information Systems, or a related field. Master's program enrollment or completion is mandatory.

Strong, demonstrable hands-on experience with AI coding assistants and the Claude API or comparable model APIs - portfolio, GitHub, or live examples strongly preferred.

A working understanding of how modern LLMs and reasoning models behave: context windows, the difference between reasoning and instruction models, and when to reach for each.

Practical experience with at least one of: building an agentic workflow, building a RAG/retrieval pipeline, or integrating models via tool calling or MCP.

Excellent prompt-engineering and context-management skills.

Coding fluency in JavaScript/React and/or Python sufficient to build, evaluate, and fix AI-generated output.

An instinct for evaluation: you want to measure whether the AI is actually correct, not just plausible.

Excellent verbal and written communication skills within a cross-functional team environment.

New graduates are encouraged to apply.

We believe exceptional talent often emerges from diverse paths. If you possess a profound curiosity, a genuine passion for continuous personal and professional growth, and a strong desire to apply your unique abilities to create significant impact within our team, we strongly encourage you to apply even if your background doesn't align perfectly with every single qualification.

Employment Type: CONTRACTOR

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