Senior Principal Data Modeler

DTCC • Jersey City, NJ

12d • Hybrid

About The Position

The Information Technology group delivers secure, reliable technology solutions that enable DTCC to be the trusted infrastructure of the global capital markets. The team delivers high-quality information through activities that include development of essential, building infrastructure capabilities to meet client needs and implementing data standards and governance. As a Senior Principal Data Modeler, you will serve as a strategic leader in shaping DTCC's enterprise data architecture and data modernization journey. You will drive the development of scalable, consistent, and business-aligned data models that enable high-quality analytics, regulatory reporting, operational efficiency, and data-driven decision making across the organization. You will collaborate with business stakeholders, data architects, engineering teams, and governance organizations to define enterprise-wide data standards, semantic models, and information architectures that support both current and future business needs. Your leadership will help establish a trusted and integrated data foundation across complex domains, platforms, and products. In this role, you will influence enterprise data strategy, promote data consistency and governance, and advance modernization initiatives spanning cloud data platforms, data products, and advanced analytics capabilities. Your expertise will be instrumental in unlocking the value of DTCC's data assets while improving interoperability, transparency, and scalability across the enterprise.

Requirements

• Minimum of 10 years of related experience
• Bachelor's degree preferred or equivalent experience
• 7+ years in data modeling and data architecture, including 5+ years in capital markets, clearing, or financial risk, with a demonstrable record as a senior, hands-on individual contributor rather than solely as a manager of modelers.
• Depth across modeling disciplines — conceptual, logical, and physical modeling; normalized and dimensional models; and event-first modeling. You understand which approach is appropriate in each context and can defend the tradeoffs when challenged.
• Risk domain fluency — position keeping, mark-to-market, exposure, margin, and VaR inputs; the full trade lifecycle, including novation, amendment, cancellation, and settlement; and genuine depth in at least one cleared asset class.
• Event-driven data design — Kafka-based architectures, event sourcing, partitioning and keying strategy, schema evolution and registry discipline, and idempotency and replay semantics.
• Temporal modeling depth — bi-temporal design that separates event time from knowledge time, point-in-time reconstruction, restatement and correction handling, and the audit trail expected by controllers and regulators. This is a differentiator, not a nice-to-have.
• Modern data platform experience — lakehouse table formats such as Iceberg or Delta over Parquet; Snowflake; and the partitioning and clustering decisions that determine whether analytical queries perform at production volume.
• Legacy-to-target migration — proven experience moving models off mainframe, monolithic, and database-driven-logic estates incrementally, using dual-run and reconciliation rather than big-bang rewrites.
• Governance and lineage — data quality controls, lineage and traceability, and fluency operating in a regulated environment where the model itself must withstand audit scrutiny.
• Influence without authority — the ability to align architects, delivery squads, and business stakeholders around a shared model, and to maintain standards across teams you do not directly manage. This is the core behavioral requirement of the role.
• Communication range — the ability to move comfortably between a whiteboard discussion with engineers and a steering conversation with executives, clearly explaining why a modeling decision has cost, risk, or delivery implications.

Nice To Haves

• Temporal modeling depth — bi-temporal design that separates event time from knowledge time, point-in-time reconstruction, restatement and correction handling, and the audit trail expected by controllers and regulators. This is a differentiator, not a nice-to-have.

Responsibilities

• Establish a unified risk data model.
• Define a canonical model for risk data and exposure across NSCC, FICC and DTC to enable consistent cross-CCP risk views.
• Enable real-time, trusted risk analytics.
• Design event-sourced and bi-temporal structures enable real time computations on the stream and restated accurately when inputs change.
• Drive consolidation and productization of risk data.
• Provide the target model and standards needed to simplify systems, databases and feeds while making risk data reusable across methodology and delivery teams.

Benefits

• Competitive compensation, including base pay and annual incentive
• Comprehensive health and life insurance and well-being benefits, based on location
• Pension / Retirement benefits
• Paid Time Off and Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree