Job Description

What is the chance?

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

📌 Senior Director, Data Architecture (Toronto)
🏢 Royal Bank of Canada
📍 Toronto