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Ventures Unlimited Inc

Lead Data Modeler

Career Insights for Data Scientist

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What they do

A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.

$105,420 / year median in Pennsylvania

+18% projected growth

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Job Description

Lead Data Modeler at Ventures Unlimited Inc Lead Data Modeler at Ventures Unlimited Inc in Devault, Pennsylvania Posted in 1 day ago.
Type:
full-time
Job Description:
Job Description Must Have Technical/Functional Skills
  • Demonstrated expertise in conceptual, logical, canonical, semantic, and physical data modeling.
  • Deep understanding of enterprise information architecture and metadata management.
  • Experience developing business-oriented canonical models independent of application implementations.
  • Strong understanding of business rule modeling, cardinality, optionality, integrity constraints, and relationship semantics.
  • Expertise in enterprise modeling patterns such as Party, Role, Agreement, and Classification.
  • Strong understanding of Supertype / subtype modeling, Temporal modeling. Associative entities, Reference data design, and Master data concepts
  • Experience supporting analytics, regulatory reporting, operational data quality, and AI-enabled business use cases through enterprise data modeling.
  • Experience establishing or contributing to enterprise data modeling, governance and stewardship functions.
  • Experience with enterprise data modeling tools such as ERwin, ER Studio, and maintaining enterprise data dictionaries.
  • Ability to create reusable business concepts and canonical models that support long-term information architecture strategy. Roles & Responsibilities
  • Partner with business data stewards and product teams to define and evolve business concepts, entities, relationships, and business rules.
  • Own the holistic Personal Wealth logical and canonical data model and maintain traceability to implementation assets.
  • Lead development of canonical models supporting householding, advisor teaming, client relationships, investment offerings, and operational workflows.
  • Support strategic initiatives such as Portfolio of the Future by designing canonical models and abstraction layers that isolate Vanguard business concepts from vendor-specific schemas.
  • Define standards and best practices for conceptual, logical, physical, canonical, and semantic data modeling.
  • Establish governance processes supporting model stewardship, versioning, lifecycle management, and change control.
  • Partner with Enterprise Data Architecture and Engineering teams to implement tooling supporting model management, metadata management, lineage, and governance.
  • Collaborate with integration teams to design Anti-Corruption Layer (ACL) patterns and mapping frameworks between vendor platforms and Vanguard canonical data models.
  • Facilitate workshops to identify, define, and validate enterprise business concepts and relationships.
  • Mentor architects, analysts, and engineers in modern data modeling practices.
  • Support data models used across advice delivery, wealth management, client servicing, analytics, regulatory reporting, and AI-enabled experiences.
  • Drive adoption of enterprise modeling standards and reusable business concepts across product and engineering teams.
  • Establish and promote common business language and shared enterprise concepts across Personal Wealth platforms.
  • Ensure canonical models provide a stable abstraction layer between business domains, internal systems, and vendor platforms. Generic Managerial Skills, If any
  • Strong business acumen with the ability to challenge assumptions and influence decisions.
  • Abili ty to facilitate modeling workshops with business stakeholders and domain experts.
  • Experience translating ambiguous business concepts into well-defined enterprise models.
  • Proven ability to balance reusable, extensible architecture with practical implementation needs.
  • Strong communication skills with the ability to influence technical and non-technical stakeholders.
Data Modeling, Modeler, Dimensional Modeling, ER studio, Erwin, Data Dictionaries, Conceptual/Logical/Physical Modeling, Semantic Modeling,