Data Modeler

Data Modeler – OneData Platform

About the Role

We are looking for a highly skilled Data Modeler to join our OneData Platform initiative—an enterprise-wide effort to centralize, govern, and operationalize data from over 35+ sources. As the Data Modeler, you will be responsible for designing conceptual, logical, and physical data models that align with business goals and technical standards.

You will work at the intersection of data engineering, data governance, and product to drive schema standardization, semantic consistency, and cross-domain scalability in Google Cloud Platform (GCP).

Key Responsibilities

Design conceptual, logical, and physical data models across Raw Silver Gold layers.

Collaborate with Data Engineers to translate models into performant BigQuery schemas.

Develop shared dimensional models, canonical entities, and fact-dimension frameworks.

Partner with Product and Analytics teams to define business-friendly data structures.

Lead schema reviews, ICD (Interface Control Document) creation, and metadata documentation.

Support data contract design between source systems and the OneData platform.

Define schema evolution strategies, data type policies, and partitioning/clustering rules.

Align models with governance, lineage, and access control standards.

Requirements

6+ years of experience in data modeling or enterprise data architecture roles.

Hands-on experience with cloud data platforms, preferably GCP (BigQuery).

Expertise in dimensional modeling techniques, including 3NF, star schema, and snowflake schema designs.

Familiarity with modeling tools like dbt.

Proficient in SQL and understanding of ELT/ETL orchestration in modern data stacks.

Strong understanding of metadata, lineage, and data governance principles.

Experience working in multi-vendor and cross-functional environments is a plus.

Nice to Have

Experience in e-commerce, retail, or Marketing data domains.

Exposure to Google Marketing Platform integrations and CDP schemas.

Familiarity with CI/CD for data models (e.g., dbt cloud).

Reduction in data model fragmentation across OneData layers.

Increased reuse of core dimensions and conformed facts across domains.

Consistent adoption of naming conventions and schema standards.

Measurable improvement in model query performance and data discoverability.