Part Time - Data Architect

Architech•Toronto, ON

30d

About The Position

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.” We support continuous growth with learning budgets, internal bootcamps, certification bonuses, summit days, and more. It’s not just work - it’s a place to grow, lead, and build things that matter. And this is just the beginning. 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.

Requirements

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.

Nice To Haves

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.

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.