London or New York

Hybrid Role | Remote Possible

This client and FS Giant is embarking on arguably The City or London's most ambitious transformation toward a next-generation data mesh architecture, this role requires visionary domain experts who can help shape the future of credit risk data management.

The VP Credit Risk Domain Data Architect will serve as the crucial bridge between world-class quantitative analysts, financial engineers, and data engineering teams. This is not a generic data architecture role; this requires deep credit risk expertise combined with advanced technical acumen to translate complex credit risk concepts into robust, scalable, and intuitive data models that power our analytical platforms and client-facing products.

• Own the conceptual and logical data architecture for our core credit ratings and risk assessment domains, ensuring they accurately capture the nuanced realities of credit analysis
• Champion domain-driven design principles as we evolve toward a federated data mesh architecture, establishing data standards and governance frameworks specific to credit risk data products
• Provide thought leadership at the intersection of financial engineering and data architecture, enabling advanced analytical capabilities including stochastic modeling, Monte Carlo simulations, and AI-driven credit assessment tools
• Serve as the ultimate authority on how credit risk concepts manifest in our data systems, from covenant structures and capital hierarchy considerations to recovery analytics and probability-of-default frameworks
• Work will directly influence how credit risk is understood, measured, and communicated across global financial markets, making this one of the most impactful roles at the nexus of finance and technology.
• Design and evolve the conceptual and logical data models for core credit risk domains, including issuer and instrument data, ratings actions, surveillance processes, and macroeconomic drivers
• Capture the intricate nuances of credit assessment in data models, including the material differences between corporate family ratings (CFR) and instrument ratings, the implications of 'Rating Watch' statuses, and the data requirements for debt cushion analyses
• Establish domain-specific data standards and governance processes for critical financial entities, ensuring consistency across analytical products and research outputs
• Develop semantically rich data products that accurately represent complex financial instruments (bonds, loans, CDS, structured credit) and their risk characteristics

Cross-Functional Leadership

• Collaborate with quantitative analysts to architect data solutions that support financial engineering initiatives, including PD/LGD/EAD frameworks, stress-testing scenarios, and credit portfolio simulations
• Partner with product teams to translate client workflow requirements into data models that power intuitive analytical tools and research platforms
• Mentor data engineers and analysts on the `why` behind the data model, not just the how, fostering deeper domain understanding across technology teams
• Evangelize data best practices and domain-driven design principles across engineering squads, influencing architectural direction without direct authority

Strategic Data Innovation

• Drive the evolution of our data infrastructure toward a mesh architecture, identifying bounded contexts and defining data product boundaries for credit risk domains
• Assess emerging technologies and techniques for enhancing credit risk data capabilities, including knowledge graphs for relationship mapping and ML-assisted data quality monitoring
• Bridge traditional financial data modeling approaches with innovative approaches that support AI/ML workflows and real-time analytics
• Contribute to the development of next-generation analytical platforms that maintain our competitive edge in credit risk assessment

Non-Negotiable Domain Expertise

• 4+ years of hands-on experience in credit risk analysis within a ratings agency, investment bank credit risk function, buy-side credit fund, or FinTech lending platform
• Intimate knowledge of the end-to-end credit assessment process and the data required to support it, including rating committee processes, surveillance methodologies, and outlook assessments
• Deep understanding of credit risk modeling concepts including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and recovery analysis frameworks
• Practical experience with complex financial instruments (bonds, loans, CDS, structured credit) and their representation in analytical systems
• Familiarity with regulatory frameworks such as Basel III's Standardized Credit Risk Assessment (SCRA) approach and their data implications

Technical Proficiency

• Expert-level data modeling skills across conceptual, logical, and physical dimensions, with mastery of various modeling techniques (normalized, dimensional, data vault)
• Proven experience designing and implementing data solutions in cloud environments (AWS preferred: S3, Redshift, Glue, Athena)
• Strong programming skills in Python (Pandas, PySpark) for data manipulation and analysis, with SQL expertise being mandatory
• Experience with modern data stack components including Spark (Databricks or EMR), Kafka for streaming, and cloud data warehousing (Snowflake, Redshift)
• Familiarity with data governance tools, metadata management platforms, and data quality frameworks

Leadership & Influence

• Proven ability to lead through influence rather than authority in complex, matrixed organizations
• Exceptional communication skills with the ability to articulate complex data concepts to quantitative modellers, business stakeholders, and engineers with equal effectiveness
• Experience mentoring and developing talent across both technical and domain dimensions
• Strategic mindset with the ability to balance immediate delivery needs with long-term architectural evolution

Technology Environment

Evolving toward a data mesh architecture, the current technology stack includes:

• Cloud Platform: AWS as our primary cloud provider, with services including S3, Redshift, Glue, Athena, and EMR
• Data Processing: Spark-based processing via Databricks, with Kafka for real-time data streaming
• Data Warehousing: Snowflake as our primary cloud data warehouse solution
• Data Integration: Custom-built ETL/ELT pipelines alongside modern SaaS integration platforms
• Analytical Tools: Python-based analytical environments with Jupyter notebooks, plus proprietary analytical tools built on React-based frontends
• Data Governance: Collibra for data cataloging and governance, with custom-built quality monitoring tools

This will be a slow transition toward a federated data mesh architecture, you will play a pivotal role in defining and driving future technical direction, including technology selection and implementation patterns for domain-oriented data products.

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