Principal Data Architect

Applied Systems Canada•,

20h•CA$200,000 - CA$250,000•Hybrid

About The Position

Applied Systems is looking for a Principal Data Architect to shape the architectural foundation of our Data and Analytics capabilities as we scale to enable better insights and lay the groundwork for AI across the enterprise. The ideal candidate brings deep data architecture expertise with a commitment to embracing AI and enabling the success of engineering teams across Applied. At Applied, Principal architects are force-multipliers — technical leaders and practitioners who are passionate about designing, guiding, and teaching by doing. You will live our Leadership Principles: owning outcomes, insisting on the highest standards, and inventing and simplifying solutions for our customers and teams. You will have an AI-First approach to architecture with a view of AI and automation as leverage to scale your impact beyond individual throughput. You will define the long-term vision for AI-enabled data architecture, design and govern agentic workflows with appropriate guardrails, and surface system-level insights that raise quality and velocity across the domain.

Requirements

15+ years in software/data engineering or architecture with outstanding impact
Degree in Computer Science, a related field, or equivalent combination of education and experience
Expert knowledge of data architecture principles and best practices
Hands-on builder experience (actively coding, testing, and validating architecture at scale)
Proven ability to influence without authority
Pragmatic approach and obsessed with eliminating waste
Player-coach mindset with a commitment to teaching through action
Adaptability, resilience, and the ability to thrive in ambiguity with iteration
Excellent communication skills, able to meet your audience where they are and explain complex problems clearly
Expertise across data and software technologies — GCP BigQuery and dbt a plus
Proven experience architecting large-scale data warehouses or lakehouses
Solid understanding of data modeling concepts including dimensional modeling and streaming architectures; experience with vector stores is a plus
Strong understanding of data governance; experience implementing data catalogs a plus

Nice To Haves

GCP BigQuery and dbt a plus
experience with vector stores is a plus
experience implementing data catalogs a plus

Responsibilities

Define large-scale data architecture decisions — batch/streaming platforms, warehouses, and lakehouses — and evaluate tradeoffs for quality, scalability, and long-term sustainability
Architect observability, alerting, and incident-response frameworks to monitor the health and SLAs of data pipelines across the organization; serve as the technical escalation point for complex data architecture problems
Partner across Applied teams to define standards for data quality, lineage tracking, access control, cataloging, and governance; establish SLAs/SLOs for critical data assets
Design data modeling standards across OLAP/warehouse (dimensional modeling), OLTP/transactional, streaming/real-time, and AI/vector/feature-store use cases
Define patterns for embedding AI and agent-supported workflows across the data engineering lifecycle, and enable teams to adopt them at scale
Define and enforce data engineering standards, best practices, and design patterns across the organization
Mentor and grow engineers through code reviews, design discussions, and technical guidance
Influence cross-functional roadmaps by translating business requirements into sound data architecture strategies