Data Modeler with Data Vault 2.0

Position Overview

We are looking for a highly skilled Data Modeler with strong expertise in Data Vault 2.0 to support the design and development of enterprise-scale data solutions. The ideal candidate will play a key role in building robust, scalable data models that support both operational reporting and advanced analytics initiatives within a cloud-based data ecosystem.

This position requires deep experience in modern data architecture, data warehousing concepts, and complex data integration environments involving transactional and file-based data sources.

Key Responsibilities

• Design and implement logical and physical data models using Data Vault 2.0 methodologies.
• Develop and maintain Raw Vault and Business Vault structures to support enterprise data initiatives.
• Create dimensional models, including fact and dimension tables, for reporting and analytics.
• Collaborate with data architects, engineers, and business stakeholders to translate business requirements into scalable data solutions.
• Design and optimize Point-in-Time (PIT) tables, bridge tables, and other advanced modeling constructs.
• Ensure data quality, consistency, lineage, and governance across the data platform.
• Support data integration efforts involving large-scale transactional and file-based datasets.
• Contribute to architecture decisions related to cloud-based data platforms and modern data warehouse solutions.

Required Experience

• 5+ years of professional experience in Data Modeling and Data Architecture.
• Strong hands-on experience with Data Vault 2.0 design principles and implementation.
• Experience designing:
  • Hubs, Links, and Satellites
  • Raw Vault and Business Vault layers
  • Fact and Dimension models
  • Data Marts and Analytical Data Structures
• Expertise in historization techniques, temporal data management, and change tracking methodologies.
• Strong understanding of transactional data modeling and enterprise data warehousing concepts.
• Experience working with cloud-based data platforms such as Snowflake, Azure Data Lake, Azure Synapse, Databricks, or similar technologies.
• Knowledge of data lineage, metadata management, and governance best practices.
• Experience handling large-volume structured and semi-structured datasets.

Preferred Qualifications

• Experience working in Healthcare, Insurance, Financial Services, or other highly regulated industries.
• Familiarity with claims, payment, billing, or other transaction-heavy datasets.
• Experience managing file-based ingestion processes and implementing change-detection strategies where CDC is unavailable.
• Understanding of Lakehouse and modern cloud data architecture patterns.