Data Modeler (Business Process Modelling)

Role Summary

We are looking for an experienced Senior Data Modeler to design and govern scalable, well-structured data models that power analytics, reporting, and application workloads across the enterprise. This role owns the end-to-end data modeling lifecycle - from conceptual and logical design through physical implementation on Google Cloud Platform (GCP) - and partners closely with data engineering, analytics, and business teams to ensure data structures are accurate, performant, and aligned to business definitions. Experience in modeling data for retail use cases (inventory, merchandising, supply chain, customer/POS data) is strongly preferred.

Key Responsibilities

• Design conceptual, logical, and physical data models for enterprise data warehouses, data lakes, and analytical platforms.
• Build and maintain dimensional models (star schema, snowflake schema) and normalized (3NF) models depending on use case, balancing query performance with maintainability.
• Translate business requirements and source system structures into robust, reusable data models and data dictionaries.
• Design and optimize data models specifically for Google BigQuery, including partitioning, clustering, and nested/repeated field strategies for performance and cost efficiency.
• Partner with data engineers to implement models within ETL/ELT pipelines (Dataflow, Cloud Composer, dbt, Dataform) and ensure models are correctly materialized in production.

• Experience in data modeling, data architecture, or data warehousing roles.
• Strong expertise in dimensional modeling (Kimball methodology), star/snowflake schema design, and normalized (3NF) modeling techniques.
• Hands-on experience designing and optimizing data models on Google Cloud Platform, particularly BigQuery (partitioning, clustering, nested/repeated fields, cost optimization).
• Proficiency with data modeling tools such as ERwin, PowerDesigner, SqlDBM, dbdiagram, or equivalent.
• Advanced SQL skills, with the ability to translate complex business logic into efficient, well-structured queries and views.
• Experience working with modern transformation frameworks such as dbt or Dataform for implementing and testing models in production.
• Solid understanding of data governance concepts: metadata management, data lineage, master data management, and data quality frameworks.
• Experience collaborating with data engineering teams on ETL/ELT pipeline design (Dataflow, Cloud Composer/Airflow, Pub/Sub).