Data Modeler

Skills: Data Modeling, AWS Glue, SQL

Duration: fulltime

Experience: 8+ years

Must Have

- Robust expertise in Data Modeling, including conceptual, logical, and physical data models for enterprise-scale data platforms.
- Deep understanding of dimensional modeling, Data Vault, star/snowflake schemas, and best practices for designing scalable and high-performing data warehouse solutions.
- Experience designing and governing enterprise data structures, metadata, master/reference data, and data quality standards across complex environments.
- Hands-on experience with AWS data services for data ingestion, transformation, storage, and consumption, including AWS Glue, Redshift, Athena, DataZone, and related services.
- Strong experience in data architecture and solution design, ensuring data models align with business requirements, analytics, reporting, and regulatory needs.
- Experience designing and implementing data models on modern lakehouse architectures using Apache Iceberg and cloud-native data platforms.
- Solid understanding of data integration patterns, ETL/ELT frameworks, and orchestration tools such as Apache Airflow.




- Experience with real-time and batch data processing, including Kafka and AWS streaming services.
- Proficiency in Python, Spark, SQL, and Infrastructure-as-Code tools such as Terraform/Terragrunt.
- Experience working with BI and analytics platforms such as Tableau, Power BI, or Amazon QuickSight, ensuring optimized data models for reporting and self-service analytics.

Nice to Have
- Experience establishing enterprise-wide data governance, data cataloging, lineage, and metadata management frameworks.
- Knowledge of Data Mesh, domain-driven data architecture, and federated data ownership models.
- Experience working within a platform/product operating model focused on reusable data products and standardized data frameworks.
- Strong understanding of Capital Markets data domains, financial products, and derivatives data structures.
- Experience defining canonical data models, reference data strategies, and enterprise information architecture standards.
- Familiarity with DevSecOps practices and automation approaches for managing and deploying data platforms at scale.

📌 Data Modeler (Sydney)
🏢 Coforge
📍 Sydney