Role Overview

We are looking for an experienced Data Modeler with strong hands-on expertise in Data Vault 2.0 architecture and methodology to design, develop, and optimize enterprise data models. The ideal candidate should have practical experience translating business requirements and source system structures into scalable, auditable, and historized Data Vault models.

The candidate will work closely with business stakeholders, data architects, and data engineering teams to define conceptual, logical, and physical data models and establish a robust Data Vault 2.0 foundation for enterprise analytics and reporting.

Key Responsibilities

·       Design and develop conceptual, logical, and physical data models using Data Vault 2.0 principles.

·       Identify and model Hubs, Links, and Satellites based on business keys, relationships, and descriptive attributes.

·       Define business keys, hash keys, hash diffs, and historization strategies.

·       Design Raw Vault and Business Vault layers, including derived structures and business rules.

·       Analyze source systems, source-to-target mappings, and business requirements to identify entities, relationships, and data integration patterns.

·       Define loading patterns for full loads, incremental loads, CDC, and late-arriving data.

·       Implement historization, auditability, traceability, and data lineage in accordance with Data Vault 2.0 standards.

·       Design Point-in-Time (PIT) tables and Bridge tables to improve downstream query performance where required.

·       Collaborate with data engineers to translate models into scalable data pipelines.

·       Support dimensional modeling and the creation of downstream data marts and semantic models.

·       Document data models, naming conventions, modeling standards, and source-to-target mappings.

·       Review existing data models and recommend improvements in scalability, maintainability, and performance.

Required Technical Skills

·       Strong practical knowledge of Data Vault 2.0 methodology and architecture.

·       Hands-on experience designing Hubs, Links, Satellites, Raw Vault, and Business Vault components.

·       Good understanding of business keys, surrogate/hash keys, hash diffs, and change detection.

·       Strong knowledge of SQL and relational database concepts.

·       Experience with source-to-target mapping, data profiling, and data integration.

·       Understanding of ETL/ELT processes, incremental loading, CDC, and historical data management.

·       Knowledge of dimensional modeling concepts, including facts, dimensions, and star schemas.

·       Experience with data modeling tools such as ERwin Data Modeler, SAP PowerDesigner, SqlDBM, or equivalent.

·       Ability to communicate technical modeling decisions to business and engineering stakeholders.

Preferred Skills

·       Experience with cloud data platforms such as Snowflake, Databricks, Azure Synapse, Microsoft Fabric, or BigQuery.

·       Familiarity with automation frameworks for Data Vault 2.0, such as dbt-based implementations or equivalent tooling.

·       Experience with orchestration and ingestion tools such as Azure Data Factory, Databricks Workflows, or similar.

·       Understanding of data governance, metadata management, data quality, and lineage.

·       Knowledge of performance optimization and scalable enterprise data warehouse architectures.

Experience and Qualifications

·       5–10+ years of overall experience in data modeling or data warehousing.

·       Demonstrable hands-on experience delivering Data Vault 2.0 models in at least one enterprise project.

·       Strong analytical and problem-solving skills.

·       Excellent communication and documentation skills.

·       Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent experience.

Expected Deliverables

·       Conceptual, logical, and physical data models.

·       Data Vault 2.0 entity identification and Hub-Link-Satellite designs.

·       Source-to-target mapping documents.

·       Raw Vault and Business Vault design specifications.

·       PIT and Bridge table designs, where applicable.

·       Data model documentation and implementation guidelines.

·       Recommendations for downstream dimensional models and reporting layers.

Ideal Candidate

A hands-on Data Modeler who can independently analyze source data, develop a robust Data Vault 2.0 model, document the design, and guide data engineers during implementation.