Senior Data Modeler - Capital Markets

Sumitomo Mitsui Banking Corporation•New York, NY

2d•$202,000 - $237,000•Hybrid

About The Position

SMBC Group is a top-tier global financial group with a 400-year history, offering diverse financial services worldwide. In the Americas, SMBC Group provides commercial and investment banking services to its corporate, institutional, and municipal clients, connecting them to local markets and its extensive global network. The Group's operating companies in the Americas include Sumitomo Mitsui Banking Corp. (SMBC), SMBC Nikko Securities America, Inc., SMBC Capital Markets, Inc., SMBC MANUBANK, JRI America, Inc., SMBC Leasing and Finance, Inc., Banco Sumitomo Mitsui Brasileiro S.A., and Sumitomo Mitsui Finance and Leasing Co., Ltd. We are seeking a highly specialized Senior Capital Markets Data Modeler to spearhead the standardization, design, and governance of our global transaction data structures across Front, Middle, and Back-Office (FOMOBO) platforms. In this role, you will bridge the gap between complex quantitative trading concepts and real-time data engineering pipelines. Your primary mandate will be to translate complex trade lifecycles—spanning execution, valuations, and downstream clearing/settlements—into modern, event-driven data schemas. By leveraging standard financial messaging protocols alongside cutting-edge lakehouse tools, you will design the data backbone supporting high-velocity stream processing frameworks.

Requirements

7+ years of experience as a specialized Data Modeler in an Investment Bank, Tier-1 Capital Markets Broker-Dealer, or Clearing House spanning Front, Middle, and Back Office operations.
Proven track record of direct, production-level implementation of ISDA CDM (Common Domain Model) and FpML frameworks for Over-The-Counter (OTC) derivatives, fixed income, or foreign exchange (FX) asset classes.
Minimum 3+ years designing data structures specifically optimized for processing on the Databricks Unified Data Analytics Platform.
Mastery of schema modeling tools (such as ERwin, InfoSphere Data Architect, Hackolade, or Idera ER/Studio).
Extensive knowledge of asynchronous message queue behaviors, schema evolution strategies (backward/forward compatibility), and stream-processing database normalization tradeoffs.
In-depth understanding of trade state mechanics handled by execution and workflow engines, such as allocation rules, novations, compression exercises, and margin calculations.

Nice To Haves

Working knowledge of SQL, Python, or Scala for testing, validating, and querying live Delta tables inside Databricks.
Prior involvement with industry bodies like FINOS, ISDA, or FIX Trading Community.
Familiarity with financial messaging formats like FIX Protocol or ISO 20022.

Responsibilities

Design End-to-End Trading Data Models: Create and maintain conceptual, logical, and physical data models capturing end-to-end multi-asset trade lifecycles across Front Office (execution/pricing), Middle Office (risk/valuations), and Back Office (settlements/reconciliations).
Implement Financial Standards (FpML & ISDA CDM): Serve as the Subject Matter Expert on FpML (Financial Product Markup Language) and the FINOS / ISDA Common Domain Model (CDM) to unify derivatives messaging and post-trade lifecycle state transitions across the enterprise.
Architect Event-Driven Schema Frameworks: Design highly performant, version-controlled streaming schemas (JSON Schema, Avro, or Protobuf) optimized for continuous pub/sub and event-sourcing architectural flows.
Optimize Databricks Lakehouse Structures: Partner with data engineers to materialize real-time event payloads into highly scalable Databricks Delta Lake tables using Medallion patterns (Bronze/Silver/Gold) optimized for time-series analytics and query performance.
Integrate Trade Workflow Engines: Model data interactions for stateful, orchestration-driven Trade Workflow Engines, ensuring exact tracking of trade status updates, validation overrides, and audit trails.
Enforce Data Lineage & Interoperability: Build comprehensive, automated data dictionaries and lineage mappings to ensure upstream trading desk schemas seamlessly reconcile with downstream regulatory reporting nodes.