Senior Manager, Data Quality & Governance USWM

RBC•Minneapolis, MN

1d•$90,000 - $160,000•Onsite

About The Position

US Wealth Management is evolving toward a domain-driven, data product-oriented architecture to enable scalable analytics, regulatory transparency, and AI-powered advisor insights. Achieving this requires moving beyond policy-driven governance to embedded, operational data control at scale. This role is responsible for turning governance into execution—ensuring data across critical domains (Client, Account, Positions, Transactions, etc.) is accurate, traceable, and fit-for-purpose. You will act as the operational lead of the Data Control Layer, embedding data quality, lineage, and governance directly into data products and pipelines, and ensuring domain teams are accountable for data outcomes.

Requirements

Strong understanding of wealth management workflows (advisor experience, client lifecycle, lending/credit, reporting)
Experience driving adoption of data platforms, analytics tools, or AI solutions
Proven ability to translate complex data concepts into business-friendly narratives and workflows
Experience designing and delivering training, enablement, or change management programs
Familiarity with domain-driven data concepts and data products
Strong stakeholder management across business, digital, and technology teams
Analytical mindset with ability to define and track adoption and value metrics

Nice To Haves

Experience with tools such as Snowflake, Power BI / Tableau
Exposure to AI/GenAI use cases in financial services (e.g., advisor insights, client intelligence, automation)
Knowledge of data governance, data quality, and metadata frameworks
Background in change management methodologies (e.g., Prosci, Agile transformation)
Experience enabling self-service analytics environments

Responsibilities

Drive Enterprise Data Adoption: Lead the rollout and adoption of data platforms, data products and AI-enabled insights across Wealth Management.
Define and execute data enablement strategies aligned to key business priorities (e.g., advisor productivity, client lifecycle, credit & lending insights).
Ensure new capabilities are operationalized, not just delivered.
Operationalize Data Products & Domains: Partner with Business Strategy, Data Product, Data Engineering, and AI teams to translate outputs into business-consumable assets.
Develop enablement frameworks for domain-based data (e.g., Client, Account, Positions, Transactions) to ensure clarity in definitions and usage.
Establish playbooks for how data products are used in real workflows, not just how they are built.
Lead Data Literacy & Training Programs: Design and deliver targeted training programs for advisors, field leadership, and business partners.
Build tiered data literacy models (basic → advanced → AI-enabled decisioning).
Enable users to confidently interpret metrics, KPIs, and AI-driven insights.
Own Communication & Engagement Strategy: Create clear, consistent communication on new data capabilities, changes, and value delivered.
Run internal campaigns to drive awareness and behavioral change.
Act as the “voice of the user” back into Data & AI teams.
Bridge Business and Data Teams: Engage Business Data Strategy team to translate business needs into enablement requirements, not technical specs.
Identify friction points in adoption and work with product/engineering teams to resolve.
Ensure alignment between what is built and what is actually used.
Measure Adoption & Value Realization: Define and track KPIs such as: Data product adoption rates, Usage frequency and depth, Advisor productivity lift tied to data usage.
Establish feedback loops to continuously improve enablement efforts.