Role Description

The Chief Data & Analytics Office (CDAO) within the SMBC Americas Division is spearheading a comprehensive transformation in data and analytics, aiming to develop industry-leading data capabilities across the organization. This strategic effort includes initiatives focused on data governance, artificial intelligence, data management, and regulatory compliance.

At SMBC, data is recognized as a critical asset that supports decision-making, risk management, and enhances customer experience. As part of the ongoing data transformation, SMBC is constructing a modern Lakehouse architecture to serve as the foundation for scalable, governed data products utilized throughout the organization. These data products are essential for driving the data transformation process, addressing both regulatory and business priorities.

To lead this transformation, SMBC is establishing a new Product Management function and is seeking a Data Product Manager to help define and implement the Lakehouse strategy. This is a hands-on role that collaborates closely with technology teams responsible for building the Lakehouse, focusing on adoption, stakeholder relationship management, and aligning priorities across the Americas Division.

Role Objectives

Lakehouse Product Roadmap
• Define, prioritize, and manage the Lakehouse product roadmap in collaboration with stakeholders from Finance, Risk, Compliance, business areas, and Operations.
• Oversee execution alongside engineering and architecture teams.
• Ensure the roadmap aligns with business objectives, technical feasibility, and stakeholder requirements.
• Manage the production and consumption of data products, machine learning datasets-including features, labels, and embeddings-throughout the data and ML lifecycle.
• Oversee data products lifecycle, ML feature stores management, focusing on organization, discoverability, and storage to guarantee efficiency, reliability, and quality.
Data Strategy & Sourcing
• Define data sourcing, labeling, and control strategies tailored to the analytical, modeling, and reporting needs of business teams.
• Ensure compliance with data governance, regulatory, data controls and data quality standards.
• Collaborate with Functional Data Offices and technology teams so that data pipelines and datasets are well-documented, governed, and equipped with observability and self-healing capabilities.
• Maintain working knowledge of the complete technology stack supporting ML training, including cloud computing, ML frameworks, and orchestration systems.
Adoption & Legacy Decommissioning
• Drive adoption of Lakehouse data products within the Americas Division by engaging consumers and integrating roadmaps, prioritization, testing, and adoption across multiple data providers and consumers.
• Facilitate the decommissioning of legacy data platforms through migration planning and stakeholder engagement.
• Develop onboarding experiences, training programs, and documentation to support adoption efforts.

Role Objectives

Product Ownership & Data as a Product
• Define and implement strategies for reusable, trusted, and discoverable data assets.
• Promote discoverability, metadata management, lineage tracking, data accessibility, security, and cataloging to support analytical, modeling, reporting, and application needs.
• Play a key role in developing and enhancing data labeling tools and processes.
Stakeholder Management & Prioritization
• Build and maintain strong relationships with stakeholders across Finance, Risk, Compliance, and business units.
• Lead prioritization discussions to ensure stakeholder needs are incorporated into the roadmap.
• Communicate platform value and progress to various audiences by regularly measuring success metrics such as adoption and productivity among analysts, modelers, and developers.
Testing & Quality Assurance
• Establish frameworks for data validation, integration testing, and performance monitoring.
• Ensure platform reliability and data integrity through collaboration with QA and engineering teams for continuous build and delivery (CI/CD) of features.

Qualifications and Skills

More than 10 years of experience in data product management within financial services or FinTech firms.
• Proven track record managing cross-functional teams and delivering data products, with solid understanding of traditional BI and ML lifecycle.
• Expertise in product management for data warehousing, traditional BI, and AI/ML platforms.
• Strong communication skills with the ability to align diverse teams to drive initiatives with clarity and speed.
• In-depth knowledge of Lakehouse and Medallion Data Architecture, streaming, and serverless data stacks.
• Hands-on experience with ML/AI development tools (such as Azure ML, AWS SageMaker, PyTorch) and cloud computing platforms (AWS, Azure, Databricks, Snowflake).
• Experience in defining, measuring, and improving productivity for analysts, data scientists, and developers.
• Demonstrated leadership in collaborating with engineers, data scientists, and ML practitioners.
• Technical fluency to lead architectural discussions and hands-on experience with data science workflows.
• Ability to develop product strategies and translate them into actionable roadmaps.
• Experience in identifying trade-offs, clarifying needs, and driving decision-making to ensure solutions create business impact.
Nice to Have
• Comprehensive understanding of data governance principles, tools, and regulatory frameworks.
• Exceptional communication, stakeholder management, and influencing skills.

SMBC's employees participate in a Hybrid workforce model that provides employees with an opportunity to work from home, as well as, from an SMBC office. SMBC requires that employees live within a reasonable commuting distance of their office location. Prospective candidates will learn more about their specific hybrid work schedule during their interview process. Hybrid work may not be permitted for certain roles, including, for example, certain FINRA-registered roles for which in-office attendance for the entire workweek is required.