Strategic Data Provisioning Specialist / Lead at Chief Data & Analytics Office (CDAO) - Assoc / VP

Join one of the world's most innovative financial institutions and help shape the future of data-driven decision-making at scale. At JPMorganChase, we believe that great data is the foundation of great outcomes — and we're looking for bold, execution-focused professionals who are ready to make a measurable impact.

As a Lead Data Engineer at JPMorganChase within the Chief Data and Analytics Office of Asset & Wealth Management, you will be at the forefront of accelerating the firm's data and analytics journey. You will partner across business, technology, and operations to make high-quality, AI-ready data available at scale — driving transparency, trust, and adoption across the data lifecycle. This is a high-visibility role where your work directly enables innovation, regulatory compliance, and client outcomes across wealth and asset management.

The Strategic Data Provisioning team is a critical enabler of AWM's data transformation — modeling behaviors that drive adoption, managing cross-product dependencies, and demonstrating value at every stage of the data lifecycle. You will bring deep technical expertise and strategic thinking to solve complex data challenges, while collaborating with senior stakeholders to deliver measurable results.

Job responsibilities

• Provision new and differentiated data assets to support AI and analytics initiatives, partnering with use case owners to define requirements, manage product dependencies, and accelerate delivery through AI-enabled data tooling

• Drive executive visibility into the progress of making critical data sources available, including performance metrics, adoption tracking, and transparency into bottlenecks

• Support agile product routines to oversee cross-product data dependencies, prioritize delivery, and align resources across business, technology, and operations partners

• Identify and document the lineage and provenance of critical data assets to support governance, regulatory, and business requirements, embedding evergreen controls to improve safety and traceability

• Develop and deliver data lineage analysis and reporting that provides executive visibility on progress against critical service-level agreements, including blockers and resourcing needs

• Lead root cause analysis of data quality issues using deep data profiling and advanced analytics techniques, fixing identified issues and embedding preventative controls to reduce future failures

• Drive operational efficiency by eliminating the cost of poor data quality through common tooling, frameworks, and proactive control development

• Enrich the metadata and semantic layer of existing data assets to improve discoverability, usability, and value for AI applications and natural language query capabilities

• Accelerate adoption of mesh data architecture by uplifting existing data assets with improved metadata, data quality scores, and lineage information

• Develop and deliver data product prototypes that demonstrate the value of enriched data assets and reduce consumer friction caused by incomplete documentation or poor catalogue quality

Required qualifications, capabilities, and skills

• Formal training or certification on data engineering concepts and advanced applied experience

• Deep subject matter expertise in wealth and asset management data domains, including customer, account, position, transaction, and/or reference data

• Proven ability to deliver results in a matrixed and complex environment, with demonstrated experience influencing stakeholders at all levels of an organization

• Experience leading strategic or transformational data initiatives, including data governance, data quality programs, or analytics transformation

• Strong technical proficiency in data profiling, analysis, and data management using modern tools and environments, including Python, R, SQL, Spark, and cloud platforms

• Experience with data quality frameworks, including profiling, rule development, issue remediation, and the design of preventative controls

• Ability to translate complex technical findings into clear executive-level communications, dashboards, and performance reporting

Preferred qualifications, capabilities, and skills

• Hands-on experience with data lineage tools and techniques, including graph databases and metadata management platforms

• Experience with data visualization and reporting tools such as Tableau or Power BI to deliver executive dashboards and performance metrics

• Knowledge of data governance frameworks, data quality dimensions, and regulatory requirements such as BCBS 239 or GDPR

• Experience applying AI and machine learning techniques to data management challenges, such as automated data profiling or metadata enrichment

• Familiarity with agile and product management methodologies, with experience working in cross-functional agile teams

• Experience with version control tools such as Git, GitHub, or Bitbucket in a collaborative engineering environment