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Part Time Databricks Architect Jobs in Toronto, ON

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 ...

Part Time Databricks Architect information

What is the difference between Part Time Databricks Architect vs Part Time Data Engineer?

AspectPart Time Databricks ArchitectPart Time Data Engineer
Primary FocusDesigning and architecting Databricks solutionsBuilding and maintaining data pipelines and workflows
Required SkillsDatabricks platform expertise, cloud architecture, SQL, SparkETL development, SQL, Spark, Python, data modeling
CertificationsDatabricks certifications, cloud certificationsNone specific, but relevant certifications helpful
Work EnvironmentCollaborates with data teams, architects, and stakeholdersWorks closely with data analysts and data scientists

While both roles involve working with Databricks and cloud data platforms, the Part Time Databricks Architect focuses on designing scalable data solutions, whereas the Part Time Data Engineer concentrates on implementing and maintaining data pipelines. Understanding these differences helps in choosing the right role based on your skills and career goals.

What are the key skills and qualifications needed to thrive as a Part Time Databricks Architect, and why are they important?

To thrive as a Part Time Databricks Architect, you need expertise in data engineering, cloud platforms (especially Azure or AWS), distributed computing, and a strong foundation in Spark and big data technologies, usually backed by a relevant degree and industry experience. Familiarity with Databricks, Python, SQL, and data pipeline orchestration tools, as well as relevant certifications like Databricks Certified Data Engineer, are typically expected. Strong problem-solving, effective communication, and the ability to collaborate with cross-functional teams set candidates apart. These skills are crucial for designing scalable data solutions and ensuring seamless integration and performance in complex business environments.

What are part time Databricks Architects?

Part time Databricks Architects are professionals who work on a flexible or reduced hour schedule to design, build, and optimize data solutions using the Databricks platform. They are responsible for architecting data pipelines, ensuring data security, and integrating Databricks with other cloud services, typically within a limited number of hours per week. These roles are ideal for organizations that need specialized Databricks expertise on a project or consulting basis, rather than a full-time commitment. Part time Databricks Architects often work remotely and may support multiple clients or projects simultaneously.

What are the typical project responsibilities for a Part Time Databricks Architect, and how does the role collaborate with cross-functional teams?

As a Part Time Databricks Architect, you are often responsible for designing scalable data solutions, optimizing cloud data pipelines, and advising on best practices for integrating Databricks into existing systems. Despite the part-time nature, you regularly collaborate with data engineers, analysts, and business stakeholders to understand requirements and translate them into technical solutions. Communication and coordination are key, as you'll need to ensure your architectural decisions align with the broader team's goals while managing your time effectively across multiple projects or clients.
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Infographic showing various Part Time Databricks Architect job openings in Toronto, ON as of July 2026, with employment types broken down into 1% Locum Tenens, 91% Full Time, 3% Part Time, 4% Contract, and 1% Summer. Highlights an 82% Physical, 7% Hybrid, and 11% Remote job distribution.

Part Time - Data Architect

Architech

Toronto, ON

Part-time

Posted 16 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.