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# Data Modeler, Transaction Data Warehouse Initiative

- **Company:** iTech US, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Databases, Data Architecture, Data Dictionary, Data Stores, Entity Relationship Models, Reference Data, Calypso Programming Language, Enterprise Data Management, Murex, KDB+
- **Published:** August 19, 2026
- **Apply:** https://www.dice.com/job-detail/5852d8cf-834a-40e8-817b-3b3eecd8854c

## About the Role

* 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)

## Description

* 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)


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