JPMorgan Chase—Data Owner Lead - Vice President

Job description

Description: If you love turning messy, complex data into something teams can use, this role is for you. JPMorgan Wealth Management needs passionate Data Owners to deliver data strategies that bring the power of our data to life. You will sit at the intersection of Product, Technology, Analytics, and Risk & Controls to deliver data that unlocks business outcomes—without losing discipline on quality and protection. As a VP Data Owner on the US Wealth Management Data & Analytics team, you will be responsible for end-to-end enablement, publishing, and governance for all data created and used by a single product. 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 the product’s data agenda across creation, ingestion, publishing, and consumption by humans and AI models - Champion product data strategy in product forums and cross-functional working groups - Establish data quality requirements and monitoring for accuracy, completeness, timeliness, and lineage - Prioritize the delivery of data pipelines and products across multiple workstreams, ensuring dependencies and priorities are executed through Agile frameworks - Document business definitions, metadata, and classification so critical data is discoverable and understood - Maintain data governance standards, classification frameworks, and compliance controls for sensitive client and financial data - Manage data risks related to protection, retention/destruction, storage, and permitted use across the lifecycle - Investigate data issues, drive root cause analysis, and deliver remediation plans with clear ownership - Measure and communicate progress through KPIs and metrics that demonstrate data quality improvements and business value realization - Influence decisions at all levels of the organization with crisp communication, strong judgment, and a bias for execution Required Qualifications, Capabilities and Skills - Bring 10+ years of experience in data delivery, analytics enablement, or data management - Demonstrate strong working knowledge of data management, data governance, big data platforms, and data architecture principles including dimensional modeling - Possess hands-on proficiency with SQL for data analysis, transformation, and quality validation across relational and big data platforms - Exhibit proven ability to prioritize effectively ambiguity in fast-paced environments with competing stakeholder demands - Communicate complex technical concepts clearly to both technical and non-technical audiences through documentation and presentations - Think analytically and systematically, breaking down complex problems into structured approaches with measurable outcomes - Understand Agile frameworks, participating actively in sprint planning, standups, and retrospectives with development teams - Collaborate effectively across organizational boundaries, building trusted relationships with diverse stakeholders and technical teams - Hold a Bachelor degree or equivalent experience Preferred Qualifications, Capabilities and Skills - 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 - Demonstrate familiarity with field-based sales environments and CRM platforms such as Salesforce, particularly in wealth management or financial advisory contexts - Apply data governance frameworks, data ethics, and responsible AI considerations in regulated environments - 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