Senior Data Management Professional - Data Modeling - Credit Data

Overview

Description & Requirements Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes.

Location London

Business Area Data

The Credit Data Modelling team sits at the core of our Fixed Income Data business, responsible for designing, evolving, and maintaining the data models that underpin the discovery, integration, and analysis of credit-related datasets. The team supports both established, production-critical data model implementations, ensuring their ongoing evolution, quality, and maintainability and newer, more exploratory technical approaches.

In parallel, the team is focused on developing novel modelling paradigms, including knowledge graphs and semantic data models, to support emerging AI-driven applications and evolving query patterns. The team partners closely with Product, Engineering, Ontologists, and Fixed Income subject matter experts to build scalable and interoperable data solutions that enable clients to navigate the complexity of credit markets with confidence, today and in the future.

We’re looking for a strong, hands-on individual contributor to join the Credit Data Modelling team. In this role, you’ll play a key part in designing, governing, and evolving the data structures that support Bloomberg’s credit data products. You’ll work with both existing data model implementations that power Bloomberg's credit data offering today, as well as the design and development of next-generation semantic and graph-based models aimed at supporting AI-driven use cases and advanced analytics.

You’ll work on a wide range of data modelling challenges, contribute to technical and design discussions, and help connect day-to-day modelling work with broader product and data strategy. As a senior member of the team, you’ll act as a subject matter expert in credit data modelling, partnering closely with Product, Engineering, Ontologists, and Fixed Income experts. You’ll help drive best practices in data governance, automation, and modelling standards, ensuring the delivery of robust, scalable, and future-proof data models.

• Design, evolve, and maintain credit-related data models and associated taxonomies, controlled vocabularies, and ontologies, covering both established production implementations and newer semantic and graph-based approaches
• Translate product and business requirements into well-structured, interoperable data models and metadata solutions, that work across datasets and delivery platforms
• Partner closely with Product, Engineering, and SMEs to co-design data models that balance legacy constraints with evolving client needs and emerging use cases
• Maintain and govern credit-related data models and metadata by auditing and evolving models and fields
• Apply and promote strong data governance practices, including adherence to FAIR principles and alignment with Bloomberg’s modelling standards
• Contribute to workflow automation, tooling, and scalable processes that improve the resilience, quality, and maintainability of our data
• Participate actively in technical discussions, reviews, and design forums, providing thoughtful input and guidance on tradeoffs and best practices
• Communicate technical concepts, progress, and priorities clearly to both technical and non-technical stakeholders
• Act as an advocate for high-quality data modelling and metadata management across the wider Credit group

• 4+ years of professional work experience in data modelling, metadata design, or semantic data structures, with the ability to work across both established production models and evolving modelling approaches
• Proven ability to work with complex, heterogeneous datasets and convert them into harmonized, queryable formats, for example, unifying…