Product Manager - Data User Experience - BQuant

Product Manager - Data User Experience - BQuant Location New York Business Area Engineering and CTO Ref # 10049958

Description & Requirements

What we do

Bloomberg is the global leader in financial data, analytics, and technology. Our products power the workflows of financial professionals across research, trading, portfolio management, and risk.

At the centre of this is BQuant. BQuant reimagines the Bloomberg experience by putting code at its core - it brings together the cutting edge of our Data, API, and AI strategies to enable unprecedented customization and time-to-value for our code-aware users, and in a manner that is instantly available. These users can quickly analyze and combine Bloomberg’s extensive financial data and analytics with their own, to build models and workflows that span the entire investment lifecycle, and easily make these available to any other user at their firm.

Your Role
As Product Lead for the Data User Experience, you will define and drive the vision, strategy, and execution of the core data and analytics experience within BQuant.

You will be responsible for how users discover, access, understand, and work with data, enabling them to combine Bloomberg data with proprietary datasets to power research, trading and AI applications - with confidence and trust.

Your customers will include both end users and internal teams building workflow-specific solutions on top of the platform, such as backtesting and trading systems integrations. You will also serve as the primary liaison to Bloomberg’s data-producing teams, and will work closely with Engineering, Quants, and other product managers to ensure the platform enables a wide range of use cases, from exploratory research to production-grade applications.

What You’ll Do

Define the Data User Experience

• Set the vision and own the execution for the end-to-end data experience in BQuant
• Own the roadmap and backlog, taking into account external and internal customer input, product strategy, value propositions, competitor analysis and operational requirements
• Balance short-term improvements with long-term platform scalability

Build a Scalable Data Analytics Platform

• Ensure data can be easily integrated, enriched, and combined with client data
• Define and evolve API abstractions for using financial and alternative datasets
• Ensure data is provisioned with appropriate SLAs, reliability, and performance
• Enable support for different workflows using batch and streaming data
• Drive consistency in how data is modeled, accessed, and used across the platform
• Ensure robust handling of entitlements, security, and data governance

Enable Quantitative and Data Intensive Workflows

• Support workflows across research, backtesting, and production deployment
• Enable users to generate signals and analytics from combined datasets
• Partner with teams building solutions on top of the data platform
• Ensure the platform supports multi-asset and complex financial data use cases
• Enable users to trust data across the full lifecycle from experimentation to production

Collaborate Across Bloomberg

• Act as the primary liaison