Senior Director, Data Architecture & Engineering

Job DescriptionWhat is the opportunity?
The Senior Director, Data Architecture simplifies and unifies RBC's data architecture across the full lifecycle: manufacturing, distribution, discovery, consumption and governance. They own the target data architecture and the standards that make data machine-readable for AI agents and operate the architecture function with agents as a core part of how it works. Success means fewer data stores and point-to-point feeds, one trusted source for each critical data element, and data products agents can find and use within their entitlements.
Key pillars of this role:
Agent Assisted Architecture operations : Run continuous inventory of data platforms and stores across RBC’s stacks. Deploy automated architecture review and decision record workflows powered by agents to scale governance and documentation
Agent governance and trust:
Establish audit trails of agent data access tied to entitlements. Implement evals to validate whether agents can answer correctly from data products. Define human-in-the-loop thresholds and ensure agents meet regulatory expectations for auditability and transparency
What will you do?
Manufacturing (data sources):
Name the authoritative source for every critical data element. Own the canonical data model and business glossary alignment. Set the standard for producer data contracts covering schema, semantics, quality rules and SLAs, so quality is fixed at the source. Establish feedback loops where agent usage and failures feedback to producers-if agents can't interpret a field, that becomes a defect against the source.
Distribution (data platform architecture):
Contribute to data distribution platform architecture and the standard ingestion patterns: batch, streaming, CDC, events and APIs. Move data once, reliably and traceably. Operate a roadmap to retire point-to-point interfaces.
Agent-assisted architecture operations:
Run continuous inventory of data platforms and stores across RBC's stacks. Deploy automated architecture review and decision record workflows powered by agents. Enable agents to assist in architecture assessment, governance documentation, and exception tracking at scale.
Discovery and machine readability:
Define what counts as a data product and the minimum metadata each must carry: meaning, owner, lineage, quality and entitlements. Build the semantic layer and ontology that lets agents interpret data, not just locate it. Set the agent-access pattern for data products, such as MCP. Include unstructured data (documents, emails, call transcripts) with appropriate chunking, embeddings and freshness strategies so retrieval-augmented workflows have high-quality context.
Consumption:
Define the consumption archetypes (BI, operational APIs, analytics and ML, RAG, agents) with one supported pattern for each. Feed curated, entitlement-aware data products into the common RAG substrate.
Agent governance and trust:
Design and implement evals for data products to validate whether agents can answer correctly from them. Establish audit trails of agent data access tied to entitlements. Define human-in-the-loop thresholds and escalation patterns. Align with model-risk governance frameworks (OSFI E-23) to ensure agents operating on data products meet regulatory expectations for transparency and auditability.
Governance by design:
Act as design authority through architecture review, decision records and exceptions. Partner with the CDO, who owns policy and accountability, while this role owns how standards are built in. Support regulatory lineage and jurisdiction-aware data placement for sovereignty.
Data quality for retrieval:
Establish quality standards and practices specific to retrieval scenarios: chunking strategies, freshness SLAs, embedding quality, and unstructured data preparation (documents, emails, call transcripts). Ensure that data products used in RAG and agent workflows meet standards for accuracy and interpretability.
Simplification and transformation:
Inventory data platforms, stores and tools across RBC's stacks and give each a disposition: tolerate, invest, migrate or eliminate. Tie the roadmap to in-flight programs such as Finance Modernization and AML Transformation, and to third-party rationalization. Track and communicate measurable outcomes: machine-readability coverage (% of critical data elements with semantics, owner, lineage and entitlements), agent task success rates on data products, and time-to-onboard new data products.
Build/buy stance on the agent layer:
Establish strategy and standards for agent infrastructure: gateway and routing, tool registry for data product access, and shared context or memory layers. Make principled decisions on build versus buy for agent-specific data infrastructure.
What do you need to succeed?
15+ years in data architecture or data engineering, including 5+ years leading architects at enterprise scale.
Depth across lakehouse and warehouse platforms, streaming and CDC, data products and mesh, catalogs and metadata, semantic layers and knowledge graphs.
A track record of measurable simplification and modernization: data feeds rationalized, costs reduced, machine-readability coverage improved and agent task success rates on data products demonstrated.
Working knowledge of regulatory frameworks including BCBS 239, privacy regulation, OSFI E-23 (model risk) and OSFI expectations for data risk.
Clear view on making data AI-ready for retrieval, entitlement propagation, agent access and unstructured data workflows.
Experience designing and implementing data product governance, metadata standards and agent-accessible data architectures.
Able to align the CDO, platform engineering, business data owners and AI/ML leadership without direct authority over them.
What's in it for you?We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.
A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
Leaders who support your development through coaching and managing opportunities
Ability to make a difference and lasting impact
Work in a dynamic, collaborative, progressive, and high-performing team
A world-class training program in financial services
Opportunities to do challenging work
#LI-POST#TECHPJ
Job SkillsArchitectural Modeling, Critical Thinking, Data Architecture Development, Data Warehousing (DW), Decision Making, Enterprise IT Architecture, Information Technology Consulting, Information Technology Trends, Multi-Level Communication, Physical Database Design
Additional Job DetailsAddress:
RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO
City:
Toronto
Country:
Canada
Work hours/week:
37.5
Employment Type:
Full time
Platform:
TECHNOLOGY AND OPERATIONS
Job Type:
Regular
Pay Type:
Salaried
Posted Date:
2026-10-27
Application Deadline:
2026-11-30
Note:
Applications will be accepted until 11:59 PM on the day prior to the application deadline date above
Our Employment OpportunitiesAt RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strides to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.
#J-18808-Ljbffr