Data Modeller

Key Responsibilities

Data Modeling & Design

•         Design, build, and maintain conceptual, logical, and physical data models for data warehouses, data lakes, and operational data stores.

•         Apply dimensional modeling techniques (Kimball star/snowflake schemas, slowly changing dimensions, conformed dimensions) and Inmon-style normalized enterprise models as appropriate.

•         Define naming conventions, modeling standards, and reusable design patterns; review peers’ models and provide constructive feedback.

•         Partner with data engineers to translate models into performant physical implementations and ELT/ETL pipelines.

Requirements Gathering & Stakeholder Engagement

•         Lead workshops with business stakeholders, product owners, and SMEs to elicit data requirements and translate them into clear modeling artifacts.

•         Document business definitions, source-to-target mappings, lineage, and data quality rules.

•         Act as a trusted advisor to analytics and engineering teams on the right modeling approach for a given use case.

Data Governance & Metadata

•         Curate and maintain metadata, business glossary terms, and data classifications in the enterprise governance platform (e.g., Collibra).

•         Support regulatory and compliance initiatives by ensuring sensitive data is properly classified, documented, and modeled.

Required Qualifications

•         6–10 years of professional experience in data modelling or a closely related data discipline.

•         Proficiency with at least one industry-standard data modeling tool (e.g., erwin Data Modeler, ER/Studio, SAP PowerDesigner, SqlDBM).

•         Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical audiences.

Preferred Qualifications

•         CDVP2 (Certified Data Vault 2.0 Practitioner) or equivalent Data Vault certification.

•         CDMP (Certified Data Management Professional) or DAMA-DMBOK familiarity.

•         Experience modeling for cloud data platforms (Snowflake, Databricks, BigQuery) and lakehouse architectures.

•         Experience with dbt, Airflow, or similar transformation/orchestration tooling.

•         Exposure to industry data models (e.g., FSLDM) relevant to the organization’s domain.

•         Familiarity with master data management (MDM), reference data, and data product / data mesh concepts.

•         Experience supporting regulatory frameworks such as CCPA, HIPAA, or SOX.