Senior Data Modeler Consultant

Responsibilities

• Assessment - Review current data models to understand the AS-IS environment.
• Conceptual, Logical, and Physical Modeling - Plan, design and maintain blueprints of the TO-BE data models for OLTP (operational), OLAP (analytical), and Enterprise Data Lakehouse environments and how data will be stored, managed and used.
• Business Alignment - Understand business processes and translate them into robust data requirements and review current report design documents to understand where data model structures can address business requirements.
• Multi-Paradigm Modeling - Designing 3NF (Third Normal Form) for transactional databases, Dimensional Models (Kimball/Star Schema) for data warehousing, and Data Vault 2.0 for scalable enterprise data lakes.
• Data Quality - Transform unstructured data into structured data by fixing errors, eliminate redundancies and unnecessary data to optimize data systems.
• Performance Optimization - Optimize query performance through smart indexing, partitioning, and materialization strategies.
• Data Governance - Establish, enforce and implement best practices, data naming standards, data definitions, maintain data maps and relevant diagrams for data systems and metadata management.
• Data Security - Specify rules and guidelines for ensuring data security and compliance. Define procedures for ensuring data are classified according to their sensitivity.
• Tooling & Documentation - Maintain data schemas using modern standards modeling tools and ensure documentation is accessible to both technical and non-technical users.
• B.Sc. Degree with a minimum of 5+ years of dedicated data modeling experience in enterprise relational databases, columnar databases.
• SQL Proficiency - Write, read, and optimize complex SQL transactional and analytical queries
• Modern Data Stack Familiarity - Experience modeling for cloud data warehouses/lakehouses (e.g., SQLSERVER, Hadoop, Snowflake, BigQuery, Databricks), working alongside transformation tools (e.g., dbt), and create ERDs for relational systems and dimensional diagrams.
• Tooling - Proficiency with ER/Studio, Erwin or similar data modeling software.
• NoSQL/Unstructured Data - Understanding of when and how to model semi-structured data (JSON, Parquet) and NoSQL databases (MongoDB, Cassandra) alongside relational data.
• Physical Data Management - Understand current data storage constrains and define TO-BE needs with growth allocation.
• Data Integration - Familiar with ETL tools to define the rules for data transformations and prepare data for big data analytics.
• Soft Skills: Communication, presentation skills, Business needs, able to lead or mentor a team.
• Certificates: W3school SQL Certification, DAMA-CDMP Certification, Microsoft: Azure Data Engineer Associate.
• Business Unit: DAIC
• Level: Mid-senior level

Additional Information

Summary Introduction: Design, implement, optimize enterprise data architecture and models that represent complex data using relational, dimensional, and NoSQL databases, and ensure data is structured for high performance, clarity, and long-term scalability across both operational and analytical systems. Data Modeler will be the translator between business stakeholders and data engineering teams.

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