Salary Range:
$140k – $175k CAD per annum
Job Class:
Full Time
Work Location:
Hybrid, Toronto
About the Company:WELL Health is an innovative technology enabled healthcare company whose overarching objective is to positively impact health outcomes by leveraging technology to empower and support healthcare practitioners and their patients. WELL has built an innovative practitioner enablement platform that includes comprehensive end to end practice management tools inclusive of virtual care and digital patient engagement capabilities as well as Electronic Medical Records (EMR), Revenue Cycle Management (RCM) and data protection services. WELL, uses this platform to power healthcare practitioners both inside and outside of WELL’s own omni-channel patient services offerings. WELL, owns and operates Canada's largest network of outpatient medical clinics serving primary and specialized healthcare services and is the provider of a leading multi-national multi-disciplinary telehealth offering. WELL, is publicly traded on the Toronto Stock Exchange under the symbol "WELL". To learn more about the company, please visit: />Position Summary:As the Senior Data Architect, you are the architectural authority and hands‑on delivery leader responsible for defining the target‑state data architecture and driving implementation outcomes. You will lead engineers directly and operate through cross‑functional squads when needed.
You will make architecture decisions, set enforceable standards, review and approve designs, and ensure the platform is delivered with high quality, security, governance, and scale.
The current enterprise standard is Snowflake,



but we are open-minded and will evaluate alternative components or stacks when there is a transparent long‑term strategic advantage (e.G., cost, scalability, developer velocity, governance, interoperability, AI enablement).
What you will be doing:1) Architectural Authority & Standards
Own the end‑to‑end architecture of the WELL Intelligence Platform and other strategic data products, as directed by the CDAIO.
Define architecture principles, reference patterns, and guardrails (batch/streaming, modeling, security, governance, observability).
Maintain Architecture Decision Records (ADRs) and lead architecture reviews.
Provide final technical approval on critical platform designs;

stop/redirect implementations that violate standards.
2) Engineering Leadership & Delivery Ownership
Lead data/platform engineers directly (technical direction, planning, prioritization, mentoring, performance feedback as applicable).
Drive delivery via squads: align cross‑functional teams (Cloud, Security, Apps, BI/Analytics) around shared outcomes.
Own technical execution plans, sequencing, and risk management for platform build phases.
Establish engineering ways‑of‑working: CI/CD, code standards, testing, release management, and operational readiness.
3) Data Modeling & Semantic Layer
Design and govern conceptual, logical,



and physical models.
Build and maintain conformed dimensions, canonical entities, and enterprise KPI definitions.
Define semantic layer patterns and ownership model so business reporting stays consistent across subsidiaries.
4) Platform Implementation (Hands‑on)
Design and guide implementation of ingestion patterns (CDC, incremental loads, event‑driven where appropriate).
Define and build reusable data pipeline frameworks, orchestration patterns, and data contract approaches.
Drive performance/cost optimization (partitioning, clustering, workload management, right‑sizing).
Establish platform observability: lineage, monitoring, alerting, SLOs/SLAs, and incident runbooks.
5) Governance, Security, and Privacy by Design
Embed privacy/security controls into architecture: least privilege, encryption, auditing, environment segregation.
Define patterns for row/column‑level security, tokenization/masking, secure data sharing, and policy‑as‑code where possible.
Ensure the platform design supports compliance expectations in data‑sensitive environments (PHI/PII).
6) AI/Automation Readiness
Ensure curated datasets are AI‑ready: traceable, reproducible, high‑quality, and appropriately governed.
Define patterns for secure retrieval and controlled access for AI use cases.
Collaborate with AI delivery teams to enable automation and agentic workflows without weakening governance.
7) Stakeholder Partnership
Translating business needs into a pragmatic backlog of data products and platform capabilities.
Align subsidiaries on standards while supporting loc

📌 Senior Data Architect (Toronto)
🏢 WELL Health
📍 Toronto