Asset Management - Data Analyst, Private Markets Data Products (Vice President)

JPMorgan Chase & Co.•New York, NY

13h•$99,750 - $215,000

About The Position

As a Data Analyst on the Asset Management Private Markets Data Products team, you will serve as the Alternatives data subject-matter expert for the domain. You will help build out the Private Markets data model and the semantic layer that sits on top of it, standardizing definitions and making data consistently usable by both people and AI. Your primary focus will be expanding Alternatives datasets to include the business-critical attributes that support core business processes and downstream consumption. You will enable client and investor reporting, oversee business-as-usual data issues and data quality, help scale the platform, and ensure data is delivered accurately to the appropriate Systems of Record (SORs). This is a hands-on, analytical role that depends on sustained collaboration with internal and external partners to understand the underlying businesses, the data they produce, and how consumers need to use it.

Requirements

Extensive experience in data-focused roles such as data analysis, data management, data operations, or business and data product analysis, within financial services or a comparably complex data environment.
Deep, practical knowledge of private markets or alternatives data and the businesses that produce it. We will also consider an exceptional data analyst from an adjacent domain who can demonstrate they learn a new business quickly and thoroughly.
Experience acting as the primary data expert for a business area or dataset, including owning its definitions and documentation and fielding questions from partner teams.
Strong hands-on analytical skills, comfortable working directly with data to profile, reconcile, and investigate, using SQL or equivalent query and analysis tooling.
Experience translating business needs into data requirements: attributes, definitions, models, and mapping specifications that engineers can build from without ambiguity.
Rigor and ownership around data quality, with a track record of driving issues to root cause and to closure across teams rather than handing them off.
Strong written communication, able to document definitions and decisions clearly enough that others can rely on them without a conversation.
Collaborative operator who works effectively across business, operations, technology, and external partners, and can move between detailed data work and senior stakeholder discussion.
Comfort with ambiguity and greenfield work, able to bring structure where no model, dictionary, or precedent yet exists.

Nice To Haves

Subject matter expertise in private markets data domains including capital calls and distributions, waterfalls and carried interest, NAV and performance, commitments and unfunded capital, investor, fund and entity structures, and deal and portfolio company data.
Experience supporting Real Estate, Infrastructure, Private Credit, Private Equity, or Alternative Solutions businesses.
Technical depth is a strong plus: advanced SQL, Python, cloud data platforms such as AWS and Snowflake, transformation tooling, and data catalog or lineage solutions.
Aptitude for AI tooling, particularly applied to data modeling and to building reusable skills and agents, with a readiness to adopt new and innovative tools to execute on deliverables and strategic projects.
Experience building or contributing to a semantic layer, metrics store, or business glossary that serves BI, self-service analytics, or AI consumption.
Experience with fund administrator, general partner, custodian, or vendor data feeds and the reconciliation and validation that comes with them.
Experience supporting client or investor reporting within an asset manager.
Familiarity with data governance concepts including data ownership, reference data and MDM, metadata, data contracts, and controls.
High aptitude with agile collaboration tools such as Confluence and JIRA, and experience working alongside a product owner and an engineering squad.
Bachelor's degree or equivalent practical experience; diverse academic backgrounds welcome. CFA, CAIA, or similar credential is a plus.

Responsibilities

Serve as the Alternatives data subject-matter expert for the Private Markets domain, acting as the reference point for what a data element means, where it originates, how it behaves, and how it should be used.
Build out the Private Markets data model alongside engineering and architecture partners, defining entities, attributes, relationships, grain, and lineage, including net-new datasets where no prior model exists.
Develop and maintain the semantic layer over the data model, covering standardized business definitions, derived metrics, and consistent vocabularies, so that data is directly consumable by people, BI tools, and AI agents without re-interpretation.
Expand Alternatives datasets to capture business-critical attributes, sequencing coverage by the core business processes and downstream consumption each attribute unlocks.
Partner with engineering in an agile framework to plan and deliver strategic work: shaping epics and stories in JIRA, authoring and reviewing data requirements and mapping specifications, answering domain questions through development, and participating in UAT and control testing.
Systematically connect vendors, fund administrators, custodians, general partners, and other external sources into the platform, onboarding feeds in a repeatable way that continues to build out the Alternatives data core rather than accumulating one-off extracts, and working with those partners to interpret data, validate it, and resolve discrepancies at source.
Partner closely with the Chief Data Office to ensure the domain adheres to firmwide and Asset Management data governance and data strategy principles, covering data ownership, classification, quality standards, documentation expectations, and the domain's contribution to enterprise data priorities.
Enable client, investor, and management reporting by translating reporting requirements into data requirements, sourcing the underlying attributes, and validating outputs against expected results.
Own business-as-usual data issue management and data quality: triage and root-cause issues, define and monitor business data quality rules and thresholds, drive remediation with source and platform teams, and surface recurring themes for structural fixes.
Ensure data is delivered accurately and completely to the appropriate Systems of Record, and support migration from legacy sources to strategic SORs including mapping, reconciliation, and cutover validation.
Profile and analyze data hands-on to test hypotheses, size gaps, and quantify impact, providing the evidence that informs prioritization, roadmap, and design decisions.
Maintain domain documentation covering data dictionaries, business glossary entries, data quality rules, lineage, and catalog and metadata entries, so the domain stays self-describing as it grows.
Help scale the platform by identifying reusable patterns and standards rather than one-off fixes as additional businesses, asset classes, and datasets onboard.