Wholesale Credit Risk Management - Data Owner - Vice President

JPMorgan Chase & Co.•Plano, TX

About The Position

As a Data Owner Lead in Wholesale Credit Risk Management team, you will play a pivotal role in executing data strategies that promote business operations and strategic objectives. You will serve as a subject matter experts, coordinating between providers and consumers to ensure business data is understood, fit for purpose, and well governed. You will collaborate with cross-functional teams to identify and prioritize data needs, ensuring data quality, protection, integration, and utilization across the organization. By leading efforts to mitigate data risks and enhance data quality, you will ensure data serves as a trusted asset for decision-making and innovation. Your leadership will foster a data-driven culture that supports organizational growth and competitive advantage.

Requirements

Bachelor’s degree in data science, Computer Science, Information Systems, Data Analytics, or a related field, OR equivalent professional experience in data management, data governance, or a related field
7 years plus of experience in data management, data governance, risk management/analytics, or a related field
In-depth understanding of data management principles, governance frameworks, and data lifecycle management, including data protection, data quality and data classification
Excellent analytical and problem-solving skills, translating complex data concepts into business strategies
Proven ability to perform data manipulation, data structuring, data design flow and query optimization using programming language including SQL and Python
Experience with public cloud-based data platforms and technologies, such as AWS, Azure, or Google Cloud
Familiarity with advanced analytics, machine learning, or artificial intelligence applications in a business context to develop new products, improve productivity, and enhance risk management
Experience in leading digital transformation initiatives that leverage data to drive business innovation and efficiency

Nice To Haves

Strong communication skills, effectively conveying data concepts to technical and non-technical stakeholders, as well as senior audiences
Strong leadership skills with experience in managing and mentoring cross-functional teams
Proven leadership track record, demonstrated ability to manage delivery timelines, and ensure the organization is on track to meet goals
Proven ability to build and maintain relationships with key stakeholders and manage large-scale data projects

Responsibilities

Implement strategic plans to deliver data solutions that effectively support business operations, strategic objectives, advanced analytics, metrics and reporting to align with organizational goals
Collaborate with product owners, business process owners, and analytics leads to identify, define, and prioritize critical data needs, ensuring effective governance, integration and utilization across the business
Collaborate with quantitative research partners to ensure that critical data is well-documented as to its meaning and purpose, and classified accordingly with metadata (data definitions, business glossary, lineage etc.) to enable its understanding and control
Document comprehensive requirements for data sourcing, content and quality, and coordinate with technology and business partners to ensure timely, complete, and accurate data
Define execution strategy to implement data mesh concepts, including following data publishing standards, ensuring governance, and making data products discoverable and interoperable across the organization.
Develop and oversee processes to identify, monitor, and mitigate data risks throughout the forecasting data lifecycle, addressing issues related to data protection, data retention and destruction, data storage, data use, and data quality
Manage the execution of projects and related milestones, understand and mitigate risks, bottlenecks, and inefficiencies throughout the project / initiative
Support data analytics and products by identifying and governing data required to be integrated into analytics platforms to support analytics projects
Communicate and ensure resolution of data issues in a timely and consistent manner, maintaining data integrity and consumer trust