Data Modeler, Transaction Data Warehouse Initiative

• Analyze and reconcile 4-5 existing instrument data models/ontologies across legacy and acquired platforms (equities, fixed income, OTC derivatives, loans) into a single canonical cross-asset model
• Define entity relationships, attribute hierarchies, and structural rules distinguishing flat instruments (e.g., equities) from structured/composite instruments (e.g., swaps, structured notes)
• Work closely with the Data Architect and Data Engineers building the physical implementation on QuestDB, ensuring the logical/canonical model translates cleanly into the physical time-series schema
• Support the reporting and analytics layers (data cubes, attribute pickers, universe selections, TCA/VWAP/TWAP benchmarks) by ensuring the canonical model can serve both transactional and analytical use cases
• Document the model thoroughly - data dictionaries, entity-relationship diagrams, mapping logic from legacy models to canonical model - since this will become a long-term system of record
• Collaborate with capital markets domain experts and technical leads to validate business correctness of the model (not just structural correctness)

• 10+ years of experience in capital markets data modeling or a closely related data architecture role
• Deep working knowledge of financial instrument types across asset classes (OTC derivatives, loans, and fixed income, including the structural differences between flat and structured instruments)
• Proven experience designing or reconciling canonical/enterprise data models, not just consuming an existing schema
• Strong data modeling fundamentals (entity-relationship modeling, normalization, hierarchy design) applicable to both relational and time-series/columnar data stores
• Comfortable working from ambiguous, partially-conflicting existing models rather than a clean-slate design brief

Nice to have:

• Direct experience with trading platforms whose data models mirror this challenge (Murex, Calypso, Summit, or similar cross-asset platforms)
• Familiarity with time-series/columnar databases (QuestDB, kdb+, or similar)
• Experience with reference data / security master concepts (golden source, instrument hierarchies)
• Background in M&A-driven system consolidation (multiple legacy models merging into one)