Senior Data Modeler

About the Role

We are looking for experienced Data Modelers who have worked on data-intensive projects in one or more of the following industries: - Telecommunications (Telco) - Financial Services & Banking - Media & Entertainment - Government/Public Sector

This is a specialist role at the intersection of data architecture, domain expertise, and AI/ML readiness. You will be responsible for designing robust, scalable data models that reflect the operational, analytical, and regulatory realities of the industries mentioned. These models will serve as the foundational structure for training ML models, building enterprise-grade data products, and driving AI transformation programs.

Key Responsibilities

• Understand domain-specific business processes, data flows, and terminologies to create accurate and practical conceptual, logical, and physical data models.

• Work closely with data scientists, ML engineers, and industry SMEs to translate use cases into structured data representations suitable for ML training.

• Design and document industry-specific ontologies, data taxonomies, entity relationships, and schemas that can support AI/ML development pipelines.

• Ensure that data models adhere to data governance, quality, and compliance standards specific to each domain (e.g., PCI-DSS, APRA, GDPR, or government data classification frameworks).

• Collaborate with stakeholders to iterate on the model based on real-world feedback, business logic, and evolving data needs.

Required Skills & Experience

• Proven experience as a Data Modeler or Data Architect on large-scale projects in one or more of the listed industries.

• Strong understanding of industry data domains, including customer data, product data, transactions, network data, media assets, case files, etc.

• Proficiency in data modeling tools (e.g., Erwin, ER/Studio, PowerDesigner, or equivalent).

• Ability to create conceptual, logical, and physical models aligned with business use cases.

• Familiarity with ML data preparation, feature engineering concepts, and how structured data models enable model training and performance.

• Strong grasp of metadata management, data lineage, and data cataloging practices.

• Excellent communication skills and ability to engage both technical and non-technical stakeholders.

Preferred Qualifications

• Experience working with AI/ML teams or product teams building AI-driven applications.

• Exposure to knowledge graphs, ontologies, or semantic modeling.

• Familiarity with data lakes, lake houses, and modern data platforms like Databricks, Snowflake, or Big Query.

• Hands-on understanding of data privacy, ethics, and regulatory concerns in respective industries.