Data Modeler

TCS•Charlotte, NC

About The Position

We are seeking a highly experienced Data Modeler to support a strategic Home Lending Data Modernization and Regulatory Reporting Transformation initiative. This program aims to modernize and simplify the Home Lending data ecosystem by migrating legacy reporting platforms and data warehouses to a scalable cloud-native architecture on Google Cloud Platform (GCP) leveraging BigQuery. The ideal candidate will possess deep expertise in Data Modeling, Home Lending, Mortgage Origination, Mortgage Servicing, FR Y-14M Regulatory Reporting, Banking Data Architecture, Data Governance, and Cloud Data Platforms. This role will be instrumental in designing enterprise-scale data models supporting regulatory reporting, risk management, analytics, data governance, and self-service reporting capabilities.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, Finance, or a related discipline.
12+ years of hands-on Data Modeling experience in enterprise environments.
5+ years of experience within Banking, Financial Services, Risk, or Regulatory Reporting domains.
Extensive experience supporting large-scale Data Warehouse Modernization or Cloud Transformation initiatives.
Strong experience in Home Lending, Mortgage Origination, and Mortgage Servicing data domains.
Proven experience supporting FR Y-14M reporting or similar regulatory reporting programs.
Expertise in: Conceptual Data Modeling, Logical Data Modeling, Physical Data Modeling, Dimensional Modeling, Data Vault Modeling, Star Schema Design, Snowflake Schema Design, Normalization & Denormalization Techniques.
Hands-on experience with Google Cloud Platform (GCP), including: BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer.
Strong experience with enterprise databases and warehouse platforms including: Oracle, SQL Server, Teradata, Hive, BigQuery.
Expertise in: Metadata Management, Data Governance, Data Quality Frameworks, Data Lineage, Master Data Management (MDM), Data Cataloging.
Strong understanding of enterprise regulatory reporting, risk reporting, and financial data governance frameworks.
Excellent stakeholder management, communication, and problem-solving skills.
Experience working in Agile delivery environments.

Nice To Haves

Experience working with large U.S. banking institutions.
Knowledge of risk management and finance data domains.
Experience supporting regulatory reporting modernization initiatives.
Familiarity with AI-assisted data discovery and metadata management tools.
Experience with Mortgage Origination Systems (LOS) and Mortgage Servicing Platforms.
Exposure to enterprise data reconciliation and data control frameworks.
Professional certifications in Data Management, Cloud Technologies, or Data Architecture.

Responsibilities

Design and maintain conceptual, logical, and physical data models for Home Lending and Regulatory Reporting domains.
Develop scalable and optimized enterprise data models for Google BigQuery environments.
Design data structures supporting: Mortgage Origination, Mortgage Servicing, Customer Information, Collateral Data, Loan Performance, Delinquency Management, Loss Mitigation, Regulatory Reporting.
Define business rules, data relationships, cardinality, and metadata standards across enterprise datasets.
Develop canonical and harmonized data models integrating multiple source systems.
Analyze, model, and document data requirements for FR Y-14M Regulatory Reporting, including: First Lien Mortgage, Home Equity, Loan-Level Performance, Borrower Information, Property Information, Servicing Data, Delinquency & Default Data.
Translate complex regulatory requirements into scalable reporting structures and data attributes.
Ensure compliance with Federal Reserve reporting standards and enterprise governance policies.
Support data lineage documentation, regulatory audits, examinations, and compliance reviews.
Partner with business and technical teams to modernize legacy warehouse and reporting environments.
Design and optimize data models across: Raw Layer, Curated Layer, Business Layer, Reporting Layer.
Define reusable data assets, enterprise data standards, and reporting datasets.
Establish enterprise naming conventions, metadata standards, and modeling best practices.
Collaborate with governance teams on: Data Quality Rules, Data Lineage, Data Controls, Reference Data Management, Data Cataloging.
Conduct data discovery workshops and data model review sessions with business stakeholders.
Present data architecture and modeling solutions to both technical and non-technical audiences.
Partner with Data Engineers, Data Architects, Product Owners, Risk Teams, and Regulatory Reporting teams to deliver scalable cloud-based solutions.