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Part Time Data Engineering Jobs in Ontario (NOW HIRING)

Our engineers and designers harness cloud-native tools, autonomous agents, data-driven insights, and GenAI to drive measurable impact - replatforming systems in the cloud, optimizing customer ...

Clerk

Windsor, ON

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

Clerk

Windsor, ON · On-site

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

Clerk

Windsor, ON · On-site

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

Clerk

Windsor, ON

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

Clerk

Windsor, ON · On-site

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

Clerk

Windsor, ON · On-site

CA$18.75/hr

Drive your future...start with a part time job; gain experience for your professional career....and ... As an entry-level role, these positions are responsible for transferring data from shipping ...

CA$114K - CA$164K/yr

Preparation of engineering calculations, design drawings, technical specifications, and ... Actual compensation for part-time roles will be pro-rated based on the agreed number of working ...

Data entry and data correction. * Performs IT-Technician duties, as required, or as needs arise ... Negotiable part-time 3 days/week up to full-time 5 days/week. * Work will be on-site. * Possibility ...

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Showing results 1-20

Part Time Data Engineering information

What are the key skills and qualifications needed to thrive as a part time data engineer, and why are they important?

To thrive as a Part Time Data Engineer, you need proficiency in programming languages like Python or SQL, knowledge of database management, and a degree in computer science or a related field. Familiarity with data warehousing tools, ETL processes, and platforms such as AWS, Google Cloud, or Apache Spark is typically required. Strong problem-solving abilities, attention to detail, and effective communication help individuals excel in this flexible role. These skills ensure accurate data pipelines, efficient data processing, and successful collaboration with cross-functional teams, even in a part-time capacity.

Are part time data engineers still in demand?

Part-time data engineers are still in demand as organizations seek flexible staffing for data pipeline development, maintenance, and analytics projects. Skills in SQL, Python, cloud platforms, and data tools remain valuable, and remote or flexible roles are increasingly available in the industry.

Can I work remotely as a part time data engineer?

Part time data engineering roles can often be performed remotely, especially when the work involves tasks like data pipeline development, database management, and cloud-based tools. Employers may require familiarity with tools such as SQL, Python, and cloud platforms, and remote work arrangements depend on the company's policies and project needs.

What is a part time data engineering job?

A part-time data engineering job involves working fewer hours than a full-time position, typically focusing on building and managing data pipelines, organizing data storage, and ensuring data quality for organizations. Part-time data engineers may work on specific projects or provide support to larger teams, often with flexible schedules. They use programming languages and tools like Python, SQL, and cloud platforms to move, transform, and optimize data. This role is ideal for those seeking work-life balance, students, or professionals looking to gain experience or supplement their income.

How does a part time data engineering role typically balance project responsibilities with limited working hours?

In a part-time data engineering position, tasks are often scoped to fit within your available hours, focusing on specific projects or maintenance work rather than broader, ongoing initiatives. You’ll likely collaborate closely with full-time engineers to ensure hand-offs are smooth and that you’re aligned on priorities. Clear communication and proactive time management are essential, as you may need to coordinate across teams or adjust your workload to meet deadlines. Many organizations also provide flexible scheduling and clear documentation practices to help part-time team members stay integrated and productive.

What is the difference between Part Time Data Engineering vs Part Time Data Analysis?

AspectPart Time Data EngineeringPart Time Data Analysis
Required CredentialsTypically requires knowledge of SQL, Python, ETL tools, and cloud platformsRequires skills in SQL, Excel, data visualization tools, and basic statistical knowledge
Work EnvironmentOften involves building data pipelines, managing databases, and working with data infrastructureFocuses on interpreting data, creating reports, and providing insights
Employer & Industry UsageUsed in tech companies, finance, and e-commerce for data infrastructure rolesCommon in marketing, consulting, and business intelligence roles across industries

Part Time Data Engineering involves developing and maintaining data pipelines and infrastructure, requiring technical skills in programming and cloud platforms. In contrast, Part Time Data Analysis centers on interpreting data, creating reports, and providing insights, often using visualization tools. Both roles are essential in data-driven organizations but differ in technical complexity and focus.

What are the most commonly searched types of Data Engineering jobs in Ontario? The most popular types of Data Engineering jobs in Ontario are:
Infographic showing various Part Time Data Engineering job openings in Ontario as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Part Time - Data Architect

Architech

Toronto, ON • On-site

Part-time

Posted 22 days ago


Job description

Join Us in Building the Future
At Architech, we don’t just ship software. We partner with North America’s leading brands to modernize legacy platforms, embed AI into real operations, and launch digital products that transform business outcomes. Our engineers and designers harness cloud-native tools, autonomous agents, data-driven insights, and GenAI to drive measurable impact - replatforming systems in the cloud, optimizing customer journeys, or accelerating AI adoption across the enterprise. You’ll work at the intersection of strategy and execution, solving complex problems alongside smart, curious teammates across Canada and Poland. Backed by 20+ years of experience, a drive for excellence, and a culture rooted in growth and collaboration, this is where you thrive if you’re looking to deliver meaningful, high-stakes software solutions.
We’re Building a More Inclusive Tech Industry
We believe diversity leads to better outcomes. Nearly half of our team was born outside of Canada, and we speak 19+ languages. We’re 31% women, 57% BIPOC, and 14% LGBTQIA+. We’ve doubled the number of women in tech roles in the past year, and maintain a 0% gender pay gap across our delivery and technology teams. Inclusion here isn’t a buzzword, it’s backed by data, policy, and accountability.
How We Work Together
We’re a close-knit, collaborative group who care about doing excellent work, and doing it with integrity. Our values shape how we show up every day:
Think Big – Dream it, plan it, ship it
Be Open & Collaborate – Diverse minds build better solutions
Never Fail a Client – Own the outcome
Grow Our People – Feedback, learning, leadership
Do the Right Thing – Even when it’s hard
Embrace Change – Adapt fast, stay curious
Our people say it best: “Employees of different backgrounds interact well within our company” - and 97% agree. Another 96% say “Architech respects individuals and values their differences.”

Data Architect (Microsoft Azure / Microsoft Fabric / AWS/ Databricks)

Role Overview

The Data Architect (Azure Data Architect or Data Platform Architect) designs and leads the build-out of scalable, AI-ready data solutions using Microsoft Fabric, Azure, or AWS with Databricks data ecosystem. This role connects business goals to data architecture, ensuring platforms are secure, performant, and optimized for analytics and AI use cases.

You will define how data flows from raw ingestion (bronze) through transformation (silver) to curated, analytics-ready models (gold). You will collaborate with engineering and business peers to design data environments that enable analytics, automation, and AI-driven insight.

Key Responsibilities

  • Architect Microsoft Fabric environments: Design end-to-end data architectures, defining ingestion, transformation, and curation patterns that support analytics and AI workloads.
  • Lead Design and implement scalable data architectures on Databricks, including lakehouse solutions leveraging Delta Lake, Unity Catalog, and medallion architecture (bronze/silver/gold layers) to support enterprise analytics and ML workloads.
  • Lead Design and manage data lake architectures - Ensure efficient replication, synchronization, and data flow across Fabric workspaces and multi-zone environments.
  • Define business-aligned data models - Partner with stakeholders to understand reporting, analytics, and AI needs and design scalable, flexible data models.
  • Define data governance, security, and access control strategies — Use Unity Catalog and Microsoft Purview for centralized metadata management, fine-grained permissions, RBAC, encryption (at rest/in transit), data masking, and lineage tracking across workspaces.
  • AI readiness - Define structures, metadata, and access patterns that make data discoverable and usable for AI workloads such as retrieval-augmented generation (RAG), intelligent search, and summarization.
  • Familarity in implementing and managing Databricks Genie to enable self-service, natural language querying of enterprise data, empowering business users with AI-driven insights.
  • Framework alignment - Ensure all data architectures align with Microsoft's Cloud Adoption Framework (CAF) and the Azure/AWS Well-Architected Framework for consistency, scalability, and governance.
  • Performance and cost optimization - Guide architecture decisions related to Fabric SKUs, OneLake storage, and data refresh strategies for efficient scale and cost.
  • Collaboration and mentorship - Work closely with Data Engineers to translate architecture into delivery, promote data quality, and ensure design consistency.
  • Documentation and enablement - Produce reference architectures, blueprints, and reusable standards that accelerate future projects and maintain governance consistency.

Skills and Qualifications

  • 7+ years of experience in data architecture, data engineering, or data platform design, including:

At least 2 years working within the Microsoft Azure data ecosystem (Fabric, Synapse, ADF, Power BI).

At least 2 years of hands-on experience with Databricks (Delta Lake, Unity Catalog, lakehouse architecture).

  • Strong grasp of data lakehouse design principles, including ELT/ETL patterns, medallion architecture (bronze/silver/gold), and schema evolution.
  • Proficiency in SQL, with working knowledge of Python for automation, validation, and pipeline scripting.
  • Hands-on experience with data governance and metadata management tools, such as Microsoft Purview and/or Databricks Unity Catalog.
  • Practical understanding of data security fundamentals — RBAC, encryption (at rest/in transit), data masking, and privacy best practices.
  • Strong communication and cross-functional collaboration skills, with the ability to translate business needs into technical architecture and work across engineering, analytics, and business teams.
  • Strategic, iterative mindset — comfortable operating in AI-ready, fast-evolving, client-facing environments.

Tools and Technologies

Microsoft Fabric, Azure Data Factory, Synapse, Power BI, Azure SQL, Databricks, Data Lake Storage, Vector DB, Microsoft Purview, Python, SQL, Git, Terraform or Bicep, Azure Monitor.

Nice to Have

  • Experience applying architecture frameworks — Microsoft's Cloud Adoption Framework (CAF) and/or the Azure/AWS Well-Architected Framework to ensure scalability and governance consistency
  • Exposure to AI/semantic data enablement — metadata enrichment, retrieval-augmented generation (RAG), knowledge graph design, vector databases, and embedding pipelines.
  • Familiarity with modern data architecture paradigms — data product thinking, domain-driven design, or data mesh principles.
  • Familiarity with vector databases, semantic search, and embedding pipelines.
  • Familiarity with our data sources — exposure to platforms such as ChurnZero, Zendesk, Salesforce, Gong, and RocketLane is a plus.

Architech is an equal opportunity employer committed to diversity. Should you require any accommodations prior to or during the interview process, please indicate this during the interview process. We strongly encourage applications from racialized people, people with disabilities, people from gender and sexually diverse communities and/or people with intersectional identities.