Senior Data Modeler Consultant

Job Description Summary

Design, implement, and optimize enterprise data architecture and models that represent complex data using relational, dimensional, and No

SQL databases. Ensure data is structured for high performance, clarity, and long‑term scalability across both operational and analytical systems. The Data Modeler will translate between business stakeholders and data engineering teams.

• 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, OLAP, analytical, and Enterprise Data Lakehouse environments.
• Business Alignment – Understand business processes and translate them into robust data requirements; review current report design documents to determine where data model structures can address business requirements.
• Multi-Paradigm Modeling – Design 3NF 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, eliminating redundancies, and eliminating 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 for data naming standards, data definitions, maintaining 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/lake houses (e.g., SQL Server, Hadoop, Snowflake, Big Query, Databricks), working alongside transformation tools such as dbt, and create ERDs for relational systems and dimensional diagrams.
• Tooling – Proficiency with ER/Studio, Erwin, or similar data modeling software.
• No
  
  SQL/Unstructured Data – Understanding of when and how to model semi‑structured data (JSON, Parquet) and No
  
  SQL databases (MongoDB, Cassandra) alongside relational data.
• Physical Data Management – Understand current data storage constraints and define TO‑BE needs with growth allocation.
• Data Integration – Familiar with ETL tools to define rules for data transformations and prepare data for big data analytics.
• Soft skills – Communication, presentation skills, business focus, ability to lead or mentor a team.

• W3
  
  Schools SQL Certification
• DAMA‑CDMP Certification
• Microsoft:
  Azure Data Engineer Associate