Data Modeller

Role Context

Own conceptual/logical/physical modelling for key domains, ensuring semantics, integrity, and performance. Enables consistent ETL implementation and supports analytics and ML-friendly structures.

Key Responsibilities

Create and maintain conceptual, logical, and physical data models.

Define canonical structures, keys, grain, relationships, reference/master data conventions.

Ensure time semantics are explicitly modelled (event_time vs process_time, snapshot dates).

Provide model-driven guidance to ETL teams and validate transformation logic alignment.

Maintain data dictionary, naming standards, domain glossary, and change impact analysis.

Ensure models support analytics reporting and ML feature derivation (events, snapshots, aggregates).

Must Have

812 years in data modelling / data architecture with strong delivery alignment.

Expertise in 3NF and dimensional modelling; canonical modelling experience preferred.

Strong SQL profiling and validation capability.

Cloud warehouse modelling experience (Snowflake/BigQuery/Redshift/Synapse).

Strong understanding of SCD, temporal modelling, surrogate keys, and conformed dimensions.

Non-technical: stakeholder communication, documentation quality, conflict resolution on definitions.

Good To Have

Exposure to Data Vault 2.0 modelling.

Experience modelling customer/marketing/event-driven domains.

Familiarity with feature-store-friendly modelling patterns