Data Owners are needed to drive data excellence through strategic ownership and technical innovation
As a Data Owner Lead within Consumer & Community Banking - Data & Analytics team, you will be responsible for end-to-end enablement, publishing, and governance for all data created. You coordinate data sourcing and usage across domains and serve as a member of the Product leadership team. You set the “rules of the road” for product data—definitions, metadata, quality expectations, and controls—so it can reliably power analytics, AI/ML decisioning, and customer experiences.
This role requires a unique blend of technical depth in data architecture and platforms combined with business acumen to drive clarity across providers and consumers, manage risk across the data lifecycle, and keep delivery moving through strong partnership and measurable outcomes.
Job responsibilities
- Own and drive the end-to-end product data strategy across creation, ingestion, publishing, and consumption (including AI/ML use cases) aligned to business objectives
- Champion data strategy across product forums and cross-functional teams, influencing decisions with clear communication and strong execution focus
- Prioritize and deliver data pipelines and products across multiple workstreams using Agile frameworks, ensuring alignment on dependencies and timelines
- Establish and monitor data quality standards covering accuracy, completeness, timeliness, and lineage
- Define and maintain data governance, classification frameworks, and compliance controls for sensitive and financial data
- Manage data lifecycle risks including protection, retention/destruction, storage, and permitted use
- Document business definitions, metadata, and classifications to ensure data is discoverable and well understood
- Drive root cause analysis of data issues and implement remediation plans with clear ownership
- Track performance through KPIs and metrics to demonstrate data quality improvements and business value realization
- Build and manage strong partnerships with stakeholders across business, technology, analytics, risk, and operations to prioritize critical data initiatives
- Support data and analytics initiatives (including AI/ML projects) while ensuring adherence to firmwide policies, standards, and regulatory requirements
Required qualifications, capabilities, and skills
- Bachelor’s degree with 10+ years of experience in data delivery, analytics enablement, or data management
- Strong expertise in data management, governance, big data platforms, and data architecture including dimensional modeling
- Proficient in SQL (and Python) for data analysis, transformation, querying, and quality validation across platforms
- Experience managing multiple workstreams with tight delivery timelines in fast-paced environments
- Proven ability to prioritize amidst ambiguity and competing stakeholder demands with strong execution focus
- Demonstrated leadership in managing products, programs, projects, or teams with accountability for outcomes
- Strong collaboration and influencing skills to build effective internal partnerships across functions
- Excellent communication skills to convey complex technical concepts to both technical and non-technical audiences
- Analytical and structured problem-solving ability to break down complex challenges into measurable outcomes
- Solid understanding of Agile methodologies with active participation in sprint planning, standups, and retrospectives
- Apply data governance frameworks, data ethics, and responsible AI considerations in regulated environments
Preferred qualifications, capabilities, and skills
- Technical knowledge of data engineering, data pipelines, data modeling, and data architecture
- Bring experience from data science, analytics, or data engineering functions within financial services institutions or management consulting firms
- Understand modern data architecture patterns including data mesh, data fabric, and domain-driven design principles
- Familiarity with field-based sales environments and CRM platforms such as Salesforce, particularly in wealth management or financial advisory contexts
- Ability to profile, wrangle, and prepare data using advanced ETL tools and languages such as Python and SQL
- Embrace a growth mindset and lifelong learning approach, staying current with emerging data technologies and industry best practices