- Define and execute product strategy, roadmap, and priorities for enterprise
data platform capabilities.
- Partner with engineering teams to deliver scalable, reliable, and secure data
solutions that support business, analytics, and AI use cases.
- Gather customer and stakeholder feedback to identify platform enhancements,
emerging needs, and innovation opportunities.
- Create and manage product backlogs, requirements, and success metrics.
- Drive adoption of modern data engineering practices and cloud-native platform
capabilities.
- Collaborate with data scientists, architects, engineers, and business
stakeholders to ensure data platforms support AI and machine learning workloads.
- Evaluate emerging technologies and industry trends to inform product strategy
and investment decisions & Define measurable business outcomes and monitor product performance against established objectives.
- Communicate product vision, roadmap progress, and strategic recommendations
to senior leadership & Support planning, prioritization, and execution of platform modernization initiatives.
- Required qualifications, capabilities, and skills
- 5+ years of experience or equivalent expertise in in Product Management,
Technology, Data Platforms, Software Engineering, or a related field.
- Strong understanding of cloud computing concepts and modern platform
architectures & Proven experience delivering enterprise-scale technology or data platform products.
- Experience working with large-scale data processing, data integration,
analytics, or data platform solutions.
- Familiarity with modern data engineering technologies such as Spark, PySpark,
Databricks, or similar distributed processing frameworks.
- Experience with enterprise data platforms, data lakes, metadata management,
or data governance solutions.
- Understanding of machine learning concepts and AI-enabled business
capabilities & Experience supporting products, platforms, or solutions used for analytics, machine learning, or generative AI workloads.
- Ability to define product requirements that enable AI-ready data assets,
model development, or data-driven decision makin & Familiarity with modern AI ecosystem concepts such as MLOps, feature engineering, model lifecycle management, or AI governance.
- Experience partnering with engineering and data science teams to deliver
AI-enabled capabilities & Knowledge of Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), or related AI technologies.
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.