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Data Product Manager – Vice President
lead · Data Science & Analytics
Join a core team driving Finance data-driven transformation!
As a Data Product Manager in the Firmwide Financial Control (FFC) team, you will safeguard the integrity of the Firm’s books and records and define the global target platform strategy, with the Transformation Team to deliver structured data models and solutions that modernize FFC operations at scale.
Job Responsibilities
- Partner with stakeholders and consumers to drive product discovery, gather and refine requirements and use cases, validate needs through regular engagement, and enable adoption through clear communications and change support.
- Own and maintain a user-centric roadmap and prioritized backlog; recommend sequencing, scope, and trade-offs to ensure the right capabilities are delivered in the right order against shared priorities.
- Translate data operations and utilization needs into clear product scope and design principles; bridge business requirements to technical delivery and acceptance criteria across the SDLC.
- Ensure underlying data is structured accurately and built for scale and reuse by supporting data sourcing, logical and physical modeling, and clear data contracts where applicable.
- Define, implement, and publish data quality rules and controls; embed data management toolsets and business process controls within the product to deliver trusted outcomes (quality, completeness, and fitness-for-use).
- Contribute to AI/ML and GenAI enablement by positioning data to be AI-ready and supporting capabilities such as anomaly flagging, trend surfacing, and impact tracing through downstream reporting and attestation outcomes.
Required qualifications, capabilities and skills
- 7+ years (or equivalent) in data product management / product ownership, data management and governance, financial data delivery, or analytics solutions within financial services or another regulated industry.
- Demonstrated experience delivering outcomes across multi-stakeholder workstreams, supported by durable routines for intake, prioritization, dependency and risk/issue management, and progress reporting.
- Working knowledge of modern data architecture concepts and patterns (logical and physical data modeling, batch and streaming, APIs, lineage, and data contracts) and how they enable trusted data products.
- Strong command of data governance and controls concepts, including governance lifecycle practices, KDE cataloging, data quality controls, usage classification, and security and access controls aligned to enterprise and regulatory standards.
- Proficiency with data and analytics tooling (including SQL) and comfort working with large, complex datasets for validation, reconciliation, analysis, and reporting.
- Strong stakeholder management and communication skills, with the ability to translate between technical and business perspectives and drive alignment and adoption.
Preferred qualifications, capabilities and skills:
- Prior experience in Finance, Financial Control, Risk, or Treasury (including General Ledger, reference data, or finance/risk data warehouse environments) within a matrixed, regulated organization.
- Familiarity with modern data platforms and ecosystems (such as AWS, Databricks, and Snowflake) and scalable architecture patterns (such as lakehouse, data mesh, and event-driven processing).
- Familiarity with AI/ML integration concepts (anomaly detection, intelligent alerting, predictive analytics, auto-generated commentary) and responsible AI considerations (bias, explainability, and hallucination risk).
- Experience designing or partnering on self-service user experiences that balance technical depth with business usability (dashboards, APIs, and conversational interfaces).
- Experience working in Agile delivery environments and collaborating effectively with cross-functional teams (product, engineering, architecture, design, and risk/control partners).
- Familiarity with Python and analytics tools is a plus.