Asset Management - Data Analyst, Private Markets Data Products; Vice President

Join J.P. Morgan Asset & Wealth Management's Asset Management team, where you can help power private markets reporting with data products built for real-world use. This role sits on the Private Markets Data Products team, supporting the delivery of alternatives data that clients, investors, and management rely on. The position is based in New York, NY (onsite) and comes with a compensation range of USD 99, per year
.

As a Data Analyst on the Asset Management Private Markets Data Products team, you'll serve as an Alternatives data subject-matter expert and help shape the Private Markets data model and semantic layer
. You'll expand Alternatives datasets, strengthen data quality, and ensure accurate delivery to Systems of Record, while translating reporting needs into data requirements and validating outputs against expected results.

• Act as the primary reference point for what Alternatives data elements mean, including where they originate, how they behave, and how they should be used.
• Build and evolve the Private Markets data model with engineering and architecture partners, defining entities, attributes, relationships, grain, and lineage, including net-new datasets when no prior model exists.
• Develop and maintain a semantic layer with standardized business definitions, derived metrics, and consistent vocabularies so data is directly consumable by people, BI tools, and AI agents.
• Expand Alternatives datasets by adding business-critical attributes, sequencing coverage according to core business processes and downstream consumption.
• Work with engineering in an agile environment by shaping epics and stories in JIRA, authoring and reviewing data requirements and mapping specifications, answering domain questions during development, and participating in UAT and control testing.
• Connect and onboard external sources (vendors, fund administrators, custodians, general partners, and others) in a repeatable way that grows the Alternatives data core rather than creating one-off extracts.
• Partner with the Chief Data Office to support firmwide and Asset Management data governance and strategy, including data ownership, classification, quality standards, documentation expectations, and contribution to enterprise priorities.
• Enable client, investor, and management reporting by converting reporting requirements into data requirements, sourcing the underlying attributes, and validating outputs.
• Own business-as-usual data issue management
  , triaging and driving to root cause and closure, defining and monitoring quality rules and thresholds, and surfacing recurring themes for structural fixes.
• Ensure accurate, complete delivery to appropriate Systems of Record
  , including migration from legacy sources with mapping, reconciliation, and cutover validation.
• Profile and analyze data hands-on to test hypotheses, size gaps, and quantify impact to inform prioritization and roadmap decisions.
• Maintain domain documentation including data dictionaries, business glossary entries, data quality rules, lineage, and catalog or metadata entries.
• Scale the platform by identifying reusable patterns and standards rather than one-off fixes as new businesses, asset classes, and datasets onboard.

• Extensive experience in data-focused roles such as data analysis, data management, data operations, or business and data product analysis in financial services or a comparably complex data environment.
• Deep, practical knowledge of private markets or alternatives data and the businesses that produce it.
• Experience serving as the primary data expert for a business area or dataset, including owning definitions and documentation and fielding questions from partner teams.
• Strong hands-on analytical skills, comfortable profiling, reconciling, and investigating data using SQL or equivalent tools.
• Experience translating business needs into data requirements, including attributes, definitions, models, and mapping specifications.
• Strong rigor and ownership for data quality, driving issues to root cause and closure across teams.
• Strong written communication skills to document definitions and decisions clearly and reliably.
• Collaborative working style across business, operations, technology, and external partners, able to shift between detailed data work and senior stakeholder discussion.
• Comfort with ambiguity and greenfield work, bringing structure where no model, dictionary, or precedent yet exists.

• Subject matter…