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.