Lead Product Manager

RBC • Toronto, ON

5d • Onsite

About The Position

The Lead Product Manager for Enterprise Data Architecture will own the product vision, strategy, and roadmap for the Enterprise Architecture Data Hubs, and Data Products, as well as ensuring that the data is ready for AI Agents. This role sits at the intersection of enterprise architecture, data engineering, and product management — responsible for translating the enterprise's application landscape and technology portfolio into governed, discoverable, and contract-driven data hub and product strategy that power analytics, AI, and business decision-making at scale. The incumbent will also drive forward the vision for Architecture as Code for data initiatives, that include the digitization of architectural capabilities and standards, Architecture Blueprints, architecture decisions, and design artifacts so that they are maintained as living products rather than static documents. The Senior Product Manager will also champion Data Product Controls and contracts with partners in Chief Data Office and Lumina, embedding Application Control Assessments, Integrated Risk Profiles, and governance guardrails into the data product lifecycle, ensuring every data product and hub is built, validated, and promoted with enterprise-grade compliance and traceability. This role requires a product leader who can bridge business stakeholders, data engineers, solution architects, and platform teams — defining what "done" looks like for data products, establishing the contracts that enforce quality and interoperability, and driving adoption across the enterprise.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Business Administration, or a related field.
• 8+ years of professional experience in product management, data architecture, data engineering, or a related discipline, with at least 3 years in a senior product management role.
• Deep understanding of Data Product and Data Mesh principles — domain-oriented data ownership, data-as-a-product thinking, self-serve data infrastructure, and federated computational governance.
• Strong knowledge of enterprise architecture frameworks — Technology Reference Models (TRM), reference architectures, architecture blueprints, and Architecture Decision Records (ADRs).
• Familiarity with AI SDLC Controls — Application Control Assessments, Integrated Risk Profiles, AI governance frameworks (NIST AI RMF, ISO 42001), and compliance integration into delivery pipelines.
• Working knowledge of data technologies — cloud data platforms (AWS/Azure/GCP), data processing (Databricks, Snowflake, Spark), streaming (Kafka/Kinesis), and data cataloging/lineage tools.
• Experience with Agile product management — writing user stories, managing backlogs, running sprint ceremonies, defining OKRs/KPIs, and using tools like Jira, Azure DevOps etc.
• Excellent stakeholder management — demonstrated ability to influence senior leaders, mediate cross-domain conflicts, and drive adoption across organizational boundaries.
• Strong communication skills — written and verbal — with the ability to present complex data architecture concepts to both technical and non-technical audiences.

Nice To Haves

• Experience with data quality frameworks — Great Expectations, Soda, Monte Carlo, or equivalent tools for data observability and contract validation.
• Familiarity with Architecture as Code — defining architecture in version-controlled, machine-readable formats (CALM, C4 Model etc.) integrated with CI/CD.
• Understanding of AI/ML data requirements — feature stores, training data pipelines, model lineage, and data provenance for responsible AI.
• Experience with data governance platforms — Collibra, Purview, Databricks Unity or equivalent for metadata management, stewardship, and policy enforcement.
• Background in financial services, banking, or other regulated industries where data governance, risk controls, and compliance are critical.
• FinOps awareness — experience with data infrastructure cost management, chargeback models, and consumption-based optimization.

Responsibilities

• Own the product vision and roadmap for Enterprise Architecture Data Hubs — defining the intake, prioritization, and delivery of hub capabilities that enable cross-domain data sharing, discovery, and consumption.
• Define and manage the Data Hub Architecture portfolio as a product, including onboarding workflows, App Code lifecycle (LeanIX factsheet creation, approval, tagging), and end-to-end traceability across the toolchain
• Drive Data Hub adoption metrics — define OKRs and KPIs for hub utilization, data product consumption, onboarding velocity, and self-service enablement; report outcomes to leadership.
• Collaborate with domain teams to understand their analytical and operational data needs and support them in publishing well-governed data products through the hub.
• Establish and enforce Data Contracts as first-class artifacts — schema contracts (structure, types, constraints), SLA contracts (freshness, availability, latency), and semantic contracts (business definitions, lineage, classification) — between producers and consumers.
• Design data product interfaces including APIs, event streams, and governed dataset endpoints, ensuring interoperability across domains and alignment with data mesh principles.
• Build and maintain a Data Product catalog with discoverable metadata, lineage, quality scores, and usage analytics — enabling self-service consumption and reducing bespoke engineering.
• Implement contract testing and validation within CI/CD pipelines — ensuring schema enforcement, anomaly detection, freshness checks, and backward-compatibility verification before promotion.
• Map and maintain the application landscape as it relates to data flows — ensuring visibility into how data moves across source systems, integration layers, hubs, and consumption endpoints.
• Curate the Technology Reference Model (TRM) for data products — defining approved technologies, patterns, and reference architectures for ingestion, storage, processing, serving, and observability.
• Own the lifecycle of Digitized Architecture Blueprints — ensuring architecture decisions, design artifacts, and reference architectures are captured as living, governed products (not static slide decks).
• Establish Architecture Decision Records (ADRs) as a standard practice — version-controlled, searchable, and linked to the data products and hubs they govern.
• Integrate architecture artifacts into the delivery pipeline — blueprints inform CI/CD stage gates, and design artifacts are validated against the TRM and reference architectures during build and promotion.
• Embed AI SDLC Controls into the data product lifecycle — ensuring every data product, hub, and agent-enabled workflow undergoes Application Control Assessment and maintains an Integrated Risk Profile.
• Define and enforce governance as a product — risk assessments, compliance checklists, audit trails, and approval workflows are productized, automated, and embedded into delivery pipelines rather than handled as manual gate reviews.
• Establish AI governance guardrails for data products that power or are consumed by AI/ML models and agentic systems — including data provenance, bias detection, model lineage, and AI Bills of Materials (AIBOMs).
• Drive adoption of policy-as-code — automated enforcement of data access policies, resource constraints, encryption standards, tagging requirements, and retention rules to reduce manual control failures.
• Serve as the primary product interface between enterprise architecture, data engineering, business domains, and platform teams — translating architectural vision into actionable product increments.
• Promote data literacy and data product adoption across the organization — define onboarding journeys, create enablement materials, and measure adoption through instrumented analytics.
• Facilitate cross-domain alignment on data contracts, shared schemas, and integration standards — mediating between data producers and consumers to resolve conflicts and establish shared ownership models.
• Present data product strategy, roadmap, and outcomes to senior leadership and architecture review boards — articulating trade-offs, risks, and investment needs with clarity.

Benefits

• A comprehensive Total Rewards Program including bonuses and flexible benefits
• competitive compensation
• commissions
• 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
• Flexible work/life balance options
• Opportunities to do challenging work
• Opportunities to take on progressively greater accountabilities
• Opportunities to building close relationships with clients
• Access to a variety of job opportunities across business and geographies

What This Job Offers

• Job Type: Full-time
• Career Level: Senior