JOB DESCRIPTION
As a Data Product Owner Lead, within the Card Data & Analytics organization, you will lead the strategy and delivery of making the firm's data ready and well-contextualized for AI and machine learning use and bring deep understanding of data platform engineering, analytics engineering, and the AI landscape to shape how our data is structured, governed, and positioned so that AI/ML solutions can consume it safely and effectively.
Job responsibilities:
- Lead the strategy for contextualizing and preparing data for AI/ML consumption, ensuring firm data is trustworthy, well-modeled, discoverable, and AI-ready.
- Serve as the connective partner between data platform engineering, analytics engineering, and AI/ML teams, translating business problems into well-defined, governed data products.
- Set standards and drive accountability for data quality, lineage, documentation, and readiness so downstream AI/ML use cases consume accurate, well-understood data.
- Partner with Product to shape roadmaps, prioritize data investments, and ensure AI-enabling work maps to measurable business value.
- Drive execution across multiple stakeholders and teams, bringing structure, planning, and discipline to complex initiatives with firm deadlines.
- Communicate clearly and influence across technical and executive audiences, translating between engineering detail and business outcomes.
- Champion data governance, privacy, and risk controls appropriate to a regulated banking environment.
Required qualifications, capabilities, and skills:
- Strong working knowledge of data platform engineering and analytics engineering concepts, including data modeling, pipelines, and modern data warehousing.
- Demonstrated fluency in the AI/ML space — a clear understanding of how AI/ML systems consume data, the role of context and metadata, and how data readiness drives outcomes.
- Proven ability to lead complex, cross-functional initiatives and hold teams accountable to timelines.
- Exceptional organizational skills and the ability to bring structure to ambiguous, fast-moving work.
- Strong communication and executive presence, with the ability to influence across engineering, product, and business partners.
- Solid product acumen and understanding of how data and AI work ladders up to business outcomes.
Preferred qualifications, capabilities, and skills:
- Experience in financial services or another highly regulated industry.
- Familiarity with modern AI enablement patterns such as retrieval, semantic/metadata layers, and grounding data for AI use.
- Experience partnering with data governance, risk, and controls functions.
