Data Owner Lead - Data Management Operations - Vice President

About the role

Join the Chief Administrative Office – Chief Data & Analytics Office and help shape the future of data management at JPMorganChase. As a Data Owner Lead, you will drive impactful data strategies and foster a data-driven culture. This role offers opportunities for career growth, skill development, and collaboration with industry-leading experts. Be part of a team that leverages AI and machine learning to support the firm’s commercial goals. In addition to strong governance execution, this role emphasizes hands-on data product delivery—bringing data together from multiple sources, shaping it into high-quality, reusable data products, and enabling broad consumer populations and advanced analytical solutions.

Responsibilities

• Implement strategic plans to deliver data solutions and data products that support business operations and strategic objectives.
• Manage project execution, mitigating risks and inefficiencies.
• Collaborate with partners to document and classify critical data with metadata.
• Ensure metadata is actionable and supports discovery and reuse (business definitions, technical metadata, lineage, quality rules/thresholds, and product documentation).
• Provide subject matter expertise on data content and usage within the business and associated product areas.
• Develop relationships with data providers and consumers across multiple functions.
• Engage broad-based consumer populations (operations, reporting, analytics, data science) to understand usage patterns and improve usability of curated datasets and data products.
• Document requirements for data sourcing, content, and quality.
• Define requirements for integrating multiple upstream sources (including mapping, transformation logic, and reconciliation) and for building curated datasets that support analytics and reporting use cases.
• Develop processes to identify and mitigate data risks throughout the data lifecycle, including data protection, privacy, retention, destruction, storage, use, and quality.
• Ensure controls are operationalized within pipelines and data products (e.g., automated quality checks, monitoring/alerting, and auditable evidence).
• Support data analytics by governing data integration into analytics platforms, including designing fit-for-purpose curated datasets and feature-ready data products for advanced analytics/ML use cases.
• Communicate and resolve data issues, maintaining data integrity; support data issue triage and root-cause analysis with business and technology partners; drive remediation and preventative controls to reduce recurrence.
• Leverage AI/ML and LLM tooling to automate governance and data management activities (e.g., metadata drafting/classification, control evidence generation, quality rule suggestions, lineage/documentation assistance), including prompt creation and evaluation; contribute to agent-based workflow designs where appropriate.

Requirements

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Data Analytics, or equivalent professional experience.
• Five years of experience in data management, data governance, or risk management/analytics.
• Demonstrated experience delivering or partnering closely on delivery of data products or curated datasets that integrate multiple sources for enterprise consumption.
• Proven leadership track record with the ability to manage delivery timelines.
• In-depth understanding of data management principles and governance frameworks.
• Working knowledge of the data development lifecycle and familiarity with the operating model for data products (build, release, run, change).
• Excellent analytical and problem-solving skills.
• Strong communication skills for technical and non-technical stakeholders.
• Strong leadership skills with experience in managing cross-functional teams.
• Proven ability to build relationships with key stakeholders and manage large-scale data projects; ability to translate consumer needs into clear data requirements, acceptance criteria, and measurable outcomes (quality, usability, adoption).

Preferred qualifications

• Experience with cloud-based data platforms such as AWS, Azure, or Google Cloud.
• Experience with data lake/lakehouse concepts and information architecture practices.
• Familiarity with advanced analytics, machine learning, or AI applications; hands-on experience leveraging LLMs for productivity and governance automation (prompt engineering patterns, agent/tooling concepts, evaluation/monitoring of outputs).
• Knowledge of query or analytical programming languages (e.g., SQL; Oracle a plus) and comfort partnering with engineering on ingestion/transformation patterns.
• Experience in leading digital transformation initiatives leveraging data.
• Experience in data product management; experience establishing operating processes for data products (documentation standards, quality SLAs/SLOs, monitoring/alerting, and lifecycle/deprecation).