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Part Time Data Jobs in Toronto, ON (NOW HIRING)

Architech

Part Time - Data Architect

Toronto, ON

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 ...

Owl.co

Part-time Data Quality Analyst

Toronto, ON · Remote

CA$20/hr

Pulling from open data sources, you will support our clients in monitoring, detecting, and preventing potential illegitimate claims. This is a remote, part-time role (15-30 hours/week)

OPENLANE

Block Clerk 2

Brampton, ON · On-site

CA$17.60 - CA$18.52/hr

... a part-time position requiring approximately 4-6 hours per week. Availability every Wednesday is mandatory. Who we're looking for: We are seeking a Block Clerk with experience in high-volume data ...

Interac Corp.

Fraud Analyst (Part-Time)

Toronto, ON · Hybrid

The vacantPart-Time Fraud Analyst (Part-Time)rolewillreport to theLeader, Fraud Operations. Fraud ... Basic data analysis skills to identify trends and anomalies * Effective investigative skills with a ...

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All JobsData JobsPart Time Data JobsPart Time Data Jobs in Toronto, ON

Part Time Data information

See Toronto, ON salary details

$8

$18

$48

How much do part time data jobs pay per hour?

As of Jul 26, 2026, the average hourly pay for part time data in Toronto, ON is $18.70, according to ZipRecruiter salary data. Most workers in this role earn between $12.39 and $18.12 per hour, depending on experience, location, and employer.

Will AI replace data analyst?

AI can automate routine data processing and analysis tasks, but data analysts are essential for interpreting complex insights, making strategic decisions, and communicating findings. The role is evolving to include skills in machine learning tools and data visualization, but human expertise remains critical for nuanced analysis. Therefore, AI is a tool that complements rather than replaces data analysts.

What are the key skills and qualifications needed to thrive as a Part Time Data Entry Clerk, and why are they important?

To thrive as a Part Time Data Entry Clerk, you need strong attention to detail, fast and accurate typing skills, and a high school diploma or equivalent. Familiarity with spreadsheet software like Microsoft Excel, data management systems, and sometimes basic database tools is typically required. Reliability, time management, and the ability to work independently are important soft skills for this role. These skills ensure that data is entered accurately and efficiently, supporting overall business operations and data integrity.

What are part-time data jobs?

Part-time data jobs are positions where employees work fewer hours than a standard full-time schedule, typically focusing on tasks related to data collection, entry, analysis, or management. These roles may include data entry clerks, data analysts, or research assistants, and are often found in industries like healthcare, finance, marketing, or education. Part-time data jobs are ideal for students, individuals seeking flexible work arrangements, or those looking to gain experience in the data field without committing to a full-time role.

Can I be a data analyst with no experience?

Entry-level data analyst positions often do not require prior experience if candidates have relevant skills such as proficiency in Excel, SQL, or data visualization tools, and a basic understanding of statistics. Gaining certifications or completing online courses can improve chances of securing such roles without previous work experience.

Are part-time data entry jobs real?

Part-time data entry jobs are legitimate positions that involve inputting information into computer systems, often requiring basic computer skills and attention to detail. These jobs are commonly found through reputable job boards and may be remote or in-office, with flexible schedules for qualified candidates.

What are some common challenges faced by part-time data professionals, and how can they be addressed?

Part-time data professionals often encounter challenges such as managing workload within limited hours, staying updated with evolving data tools, and integrating effectively with full-time team members. To address these, clear communication about project expectations and deadlines is essential. Utilizing collaborative tools and participating in regular team meetings can help part-time staff stay aligned and contribute effectively, while ongoing learning helps bridge any skills gaps. Many organizations also encourage part-time employees to attend training sessions or workshops to stay current.

Is 40 too late for data science?

Age is not a strict barrier for data science roles, and many professionals transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and tools like Python or R, along with practical experience or certifications. Employers value diverse backgrounds and experience, so starting at 40 can still lead to a successful data science career with dedicated learning and networking.

What is the difference between Part Time Data vs Part Time Data Analyst?

AspectPart Time DataPart Time Data Analyst
Required CredentialsBasic understanding of data concepts, possibly some courseworkRelevant degree or certification in data analysis, statistics, or related field
Work EnvironmentFlexible, often remote or freelance projectsOffice or remote, focused on analyzing data sets
Employer & Industry UsageVarious industries, including tech, marketing, researchBusinesses seeking data insights, analytics firms, research organizations

Part Time Data generally refers to flexible, entry-level data-related tasks, while Part Time Data Analyst involves more specialized analysis work requiring relevant skills and certifications. The analyst role typically demands a deeper understanding of data tools and methodologies, making it more focused on interpreting data to inform decisions.

What are the most commonly searched types of Data jobs in Toronto, ON? The most popular types of Data jobs in Toronto, ON are:
What are popular job titles related to Part Time Data jobs in Toronto, ON? For Part Time Data jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Part Time Data jobs in Toronto, ON look for? The top searched job categories for Part Time Data jobs in Toronto, ON are:
What cities near Toronto, ON are hiring for Part Time Data jobs? Cities near Toronto, ON with the most Part Time Data job openings:
Part Time Data jobs near you
Infographic showing various Part Time Data job openings in Toronto, ON as of July 2026, with employment types broken down into 100% Part Time. Highlights an 87% In-person, and 13% Remote job distribution, with an average salary of $38,886 per year, or $18.7 per hour.
Part Time - Data Architect

Part Time - Data Architect

Architech

Toronto, ON

Part-time

Posted 12 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.