Location: Charlotte, NC
Salary: $98.00 USD Hourly - $103.00 USD Hourly
Description: Senior Data Modeler / Domain Model Lead - Enterprise Common Domain Model (CDM), Capital MarketsLocation: Charlotte, NC (550 S Tryon St)
Work Arrangement: Hybrid - 3 days per week in office
Contract Duration: Through end of 2026, with potential for extension and/or conversion to full-time
Job OverviewWe are seeking a
Senior Data Modeler / Domain Model Lead to serve as the
enterprise owner of the Common Domain Model (CDM) for Capital Markets and Investment Banking. This role functions as an internal enterprise product owner, responsible for canonical data model stewardship, governance, and enterprise-wide adoption across asset classes.
The CDM underpins interoperability across front office, risk, finance, operations, regulatory reporting, and analytics platforms. The ideal candidate brings deep capital markets domain expertise, strong data modeling discipline, and a control-oriented mindset for governance, versioning, lineage, and auditability.
Experience aligning internal enterprise models with
ISDA CDM and leveraging
FINOS ecosystem practices (standards-based modeling, open collaboration, and interoperability patterns) is strongly preferred.
Key Responsibilities Enterprise CDM Ownership & Strategy
- Own the enterprise CDM vision, scope, and roadmap for cross-asset capital markets domains across the full trade lifecycle.
- Define and maintain domain decomposition and bounded contexts to ensure consistent semantics across lines of business and platforms.
- Establish and enforce CDM guiding principles, including canonical semantics, implementation-agnostic logical modeling, interoperability, and auditability.
Canonical Data Modeling (Cross-Asset, Trade Lifecycle, Post-Trade)
- Design and maintain conceptual, logical, and canonical models covering:
- Products (rates, credit, FX, equities, securitized products, as applicable)
- Trades, positions, lifecycle events, allocations, valuations, and cashflows
- Parties and legal entities, accounts, agreements (including CSA), settlement instructions, and reference data
- Normalize identifiers and hierarchies (e.g., trade identifiers, UTI/USI considerations, party and instrument identifiers) to support reconciliation and interoperability.
- Define event-driven and state-based lifecycle modeling patterns for consistent downstream processing.
CDM Governance, Controls & Operating Model
- Establish and operate CDM governance processes suitable for a regulated financial environment, including:
- Design authority participation and steward/owner workflows
- Approval processes and audit-ready documentation
- Enforce modeling standards (naming conventions, definitions, relationships, cardinality, constraints).
- Maintain comprehensive metadata, lineage, and glossary alignment.
- Manage schema and model versioning using controlled release processes, including:
- Semantic versioning
- Backward compatibility and deprecation strategies
- Release notes and migration guidance for consumers
- Impact analysis for breaking changes
- Define canonical-to-physical implementation patterns (e.g., lakehouse tables, APIs, event schemas) without tying the CDM to a single platform.
Enterprise Adoption Enablement
- Drive enterprise-wide adoption of the CDM by delivering:
- System onboarding and mapping templates
- Canonical transformation patterns (ingest ? standardize ? validate ? publish)
- Reference examples, including sample payloads, canonical entities, and lifecycle events
- Partner with architecture, engineering, and platform teams to ensure consistent and practical CDM implementation.
- Facilitate design reviews to prevent conflicting definitions and unauthorized "shadow models."
AI / LLM Enablement
- Ensure the CDM supports AI, ML, and LLM use cases by:
- Designing AI-ready canonical datasets and curated training views
- Enabling semantic layers for retrieval-augmented generation (RAG) and knowledge retrieval
- Supporting unstructured-to-structured data extraction patterns (e.g., confirmations or agreements to canonical terms)
- Collaborate with AI governance and model risk teams to support:
- Reproducibility through versioned datasets aligned to CDM releases
- Explainability via clear definitions and lineage
- Audit-ready documentation of semantic decisions
Required Qualifications- 5+ years of hands-on data modeling experience within an investment bank, broker-dealer, or capital markets technology organization.
- Experience supporting front-to-back workflows including Front Office, Risk, Operations, Finance, and Regulatory Reporting.
- Demonstrated ownership or significant contribution to an enterprise or canonical Common Domain Model used across multiple systems or lines of business.
- Proven ability to lead cross-functional working sessions to drive agreement on canonical semantics and resolve conflicting definitions.
- Experience defining canonical entities and relationships for capital markets domains such as:
- Trades, positions, lifecycle events, valuations, and cashflows
- Parties, legal entities, accounts/books, agreements/CSAs, and reference data
- Track record of driving enterprise adoption, including system onboarding, mappings, and semantic harmonization.
- Hands-on experience with data mapping and harmonization across heterogeneous trading and post-trade platforms (vendor and proprietary).
- Experience supporting risk and regulatory use cases requiring consistent canonical semantics and strong controls.
Preferred Qualifications- Hands-on familiarity with ISDA CDM concepts and lifecycle modeling patterns.
- Experience mapping internal enterprise models to ISDA CDM and defining extensions.
- Familiarity with FINOS ecosystem practices related to domain modeling and interoperability.
- Experience integrating canonical models into:
- Streaming and event-based architectures
- Lakehouse or data warehouse platforms
- API and message contract governance frameworks
- Exposure to AI/LLM enablement in regulated environments, including semantic layers and curated training datasets.
Core Skills & Competencies- Cross-asset canonical data modeling
- Strong understanding of capital markets trade lifecycles
- Governance and change management leadership
- Control-oriented mindset focused on auditability, lineage, and reconciliation
- Versioning and compatibility management
- Ability to translate complex business semantics into implementable models
- Clear, concise documentation and stakeholder communication
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