Senior Director, Enterprise Architecture - Data & AI

Overview

Why Colliers?

Accelerate your success  one of Canada’s leading commercial real estate services firms, we are driven by our mission to accelerate success for our people, clients, and communities. A career at Colliers gives you the opportunity to make an impact, advance your career, and collaborate with top-tier talent. We’ve been recognized as a top employer on a number of prestigious lists, including Best Workplaces in Canada, Best Workplaces for Women, Best Workplaces with Most Trusted Executive Teams, among others.

Hybrid Role Toronto, Ontario or Vancouver, BC

We are hiring a transformational yet pragmatic leader to architect and scale Colliers’ data-to-AI platform on Google Cloud. This role is central to our digital transformation. You will turn our GCP standardization and Gemini Enterprise initiatives into durable capabilities that deliver trusted data, governed GenAI, and measurable business impact — without accruing technical debt.

As the Senior Director of Data & AI Architecture, you will set the multi-year strategy, make rigorous architecture decisions, and guide cross-functional teams to ship outcomes that executives can trust and frontline teams can use. You will have strategic autonomy, executive support, and a global team committed to innovation.

• Define, own, and champion the enterprise data & AI architecture vision and roadmap, ensuring direct alignment with strategic business priorities.
• Lead the architectural design of a cloud-native data & AI platform on GCP (Big Query, Vertex AI, Pub/Sub, Dataflow), incorporating reference patterns for Snowflake, Databricks, and Kafka where appropriate.
• Establish and govern the financial operations (Fin Ops) for the platform, driving cost-optimization, budget predictability, and tracking unit economics (e.g., LLM request cost, Big Query slots).
• Accelerate the enterprise adoption of Gemini on Google Cloud by defining the technical architecture for governance, usage analytics, and secure integration with enterprise data.
• Architect grounded AI patterns (Vertex AI Search, RAG) with context caching and cost controls to balance innovation with sustainability.
• Standardize LLMOps practices and responsible AI guardrails aligned to NIST AI RMF and EU AI Act, including patterns for automated evaluation, red-teaming, and jailbreak defenses. Act as a key technical leader within the AI Governance forum, shaping policy in partnership with Legal, Compliance, and Risk.
• Institutionalize the Google Cloud Dataplex Universal Catalog to publish certified data products, enforce column-level lineage, and automate data quality rules with alerting
• Adopt Open Lineage to capture runtime pipeline lineage from tools like Dataflow and Spark, accelerating root-cause and impact analysis.
• Architect a defense-in-depth security posture using VPC Service Controls, private ingress/egress, and Sensitive Data Protection (DLP) to mitigate data exfiltration risks.
• Apply Confidential Computing (Confidential VMs/GKE) for data-in-use protection during sensitive analytics and ML workloads.
• Serve as a trusted advisor to business and technology leaders, translating complex strategies into clear, outcome-oriented plans.
• Build, lead, and mentor a high-performing community of practice for architects and engineers across Data, Enterprise Architecture, and AI/ML functions.

• 12–15+ years across data architecture, engineering, and analytics, with 3+ years in an enterprise leadership role leading large-scale modernization.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• A proven track record of delivering governed GenAI and data platform capabilities from strategy through execution.
• Deep, hands-on knowledge of GCP data services (Big Query, Vertex AI, Dataplex, Pub/Sub, Dataflow) and multi-cloud awareness (Azure/AWS).
• Google Cloud Professional Data Engineer/Architect, TOGAF, DAMA CDMP.
• Experience with enterprise enablement, admin controls, adoption analytics, long-context RAG patterns, and cost optimization.
• Mastery of Dataplex for creating data products and enforcing column-level lineage, supplemented by Open Lineage for runtime…