Data Modeler

Zensys; City of Melbourne; On-site; AUD 180,000 - 230,000; Full time

Job summary

Zensys is seeking a senior data modeling and governance specialist in Melbourne to drive analytics data products. You will shape conceptual/logical/physical models, define data quality rules, and steward CDEs.

The role collaborates with data engineers on Dataflow/Dataproc/Composer to optimize BigQuery for performance and cost. With 8–10+ years in data modeling and governance, you will own complex data landscapes, implement Kimball dimensional modeling, and advance data lineage and stewardship

Qualifications

• 8–10+ years in data modeling and governance.
• At least 3+ years on Google Cloud Platform (GCP).
• Strong SQL across analytical warehouses (BigQuery preferred).
• Proven delivery of conceptual/logical/physical models for analytics & data products.
• Deep knowledge of dimensional modeling (Kimball) and familiarity with Data Vault 2.0.
• Hands‑on Collibra: configuring communities/domains, governance, glossary, lineage ingestion, and workflows.
• Practical stewardship: defining CDEs, data quality rules/thresholds, and access approvals.
• Experience collaborating with data engineers on Dataflow/Dataproc/Composer, optimizing BigQuery for performance & cost.

Skills

Data modeling

Data governance

SQL

BigQuery

Dimensional modeling (Kimball)

Data Vault 2.0

Collibra

Data stewardship

Data quality rules

Data lineage

Data products

Cloud data platforms

Dataflow/Dataproc/Composer

Tools

Collibra

Dataflow

Dataproc

Composer

BigQuery

Job description

• 8–10+ years overall in data modeling/governance with at least 3+ years on GCP; strong SQL across analytical warehouses (BigQuery preferred).
• Proven delivery of conceptual/logical/physical models for analytics & data products; deep knowledge of dimensional modeling (Kimball) and familiarity with Data Vault 2.0.
• Hands‑on Collibra: configuring communities/domains, operating model, business glossary, policies, lineage ingestion, issue management, and stewardship workflows.
• Practical stewardship: defining CDEs, data quality rules/thresholds, certification criteria, and data access approvals.
• Experience collaborating with data engineers on Dataflow/Dataproc/Composer, and optimizing BigQuery for performance & cost.