Snowflake Data Platform for Credit Analysis and Portfolio Management

The client ran fixed income credit analysis and portfolio management across fragmented systems, where security-issuers were identified only by vendor-specific tickers and analyst coverage, security mappings, and index data lived in disconnected, manually maintained tables. This gap held back the company's strategy of becoming a data-driven, AI-ready organization: reconciling security-issuers across source systems (SimCorp Dimension, Bloomberg, index providers), historizing coverage, and linking securities to their security-issuers were all unreliable.

Templefield Technologies was engaged to build a central, integrated data foundation that fulfills the company's data strategy and provides the trusted, AI-ready basis for future ML use cases. This required a company-owned inventory of security-issuers, resolution of identifiers across heterogeneous sources, and full historization of analysts, coverage, securities, and index data — migrating existing draft Snowflake SQL into declarative, testable dbt models while routing ambiguous matches to manual review instead of failing pipelines.

• Built a state-of-the-art dbt project on Snowflake, migrating imperative draft SQL into a declarative, CORE-first design
• Delivered a normalized master-data model of unique business entities plus a galaxy (fact-and-dimension) marts layer for fast analytics
• Created a company-owned inventory of security-issuers and resolved identifiers across systems (vendor tickers, SimCorp Dimension partners, ISIN/CUSIP/FIGI), routing ambiguous matches to an exception model
• Modeled analysts, coverage, and a canonical security master (iBoxx, ICE ML, SimCorp Dimension) with full historization
• Applied deterministic keys, dbt tests, and reconciliation analyses, reusable across DEV → UAT → PROD with CI/CD and Dagster

This engagement is in progress, running April–December 2026. Templefield is building a repeatable, test-covered data foundation that consolidates security-issuers, identifiers, references, analysts, coverage, securities, and mappings into governed Snowflake tables. The target outcome is an unambiguous, fully historized source of truth for fixed income credit analysis, with automated matching, controlled handling of data-quality exceptions, and a trusted foundation for downstream applications and future ML-driven use cases across DEV, UAT, and PROD.