Data and AI Modeler

Welcome to MultiBank Group , a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, deep liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.

Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering clients to achieve their financial goals.

Role Overview

We are looking for a Data and AI Modeler to own the design, implementation, and governance of our data models, ensuring that every AI system, BI dashboard, and analytical workload is built on a foundation of clean, well-structured, semantically consistent data. This is a specialist role at the intersection of data architecture and AI enablement.

You will design dimensional models, data vault schemas, semantic layers, and feature stores that serve both business intelligence consumers and machine learning systems. You understand that data model quality is a force multiplier for every downstream AI and analytics use case.

Key Responsibilities

Data Modelling and Architecture

• Design and implement enterprise data models including star schema, snowflake schema, Data Vault 2.0, and wide denormalised models optimised for different consumption patterns.
• Own the data model for the organisation's core business domains including users, transactions, products, events, and financial data, ensuring models are consistent, extensible, and well-documented.
• Collaborate with data engineers to ensure physical implementation of data models performs efficiently at scale on Databricks, Snowflake, or BigQuery.

Semantic Layer and Metric Governance

• Design and maintain the organisation's semantic layer using dbt Metrics, LookML, or similar, establishing canonical metric definitions, dimension hierarchies, and business logic in a governed and reusable layer.
• Enforce metric governance: every key business metric has a single, documented, auditable definition consumed consistently across BI, reporting, and AI systems.
• Collaborate with BI and analytics teams to expose the right data models through BI tools with appropriate access control and documentation.

AI and ML Feature Data Architecture

• Design and build the data foundation for ML feature engineering, producing clean, reliable, point-in-time correct feature datasets for training and inference.
• Architect and implement feature store integration using Feast, AWS SageMaker Feature Store, or custom solutions with offline historical and online low-latency serving components.
• Ensure ML training datasets are free of data leakage, have proper temporal splits, and are produced through reproducible, version-controlled pipelines.

Data Quality and Lineage

• Implement data quality frameworks using dbt tests and Great Expectations across all core data models, ensuring schema validation, null rate monitoring, referential integrity, and business rule checks run automatically.
• Build and maintain data lineage documentation tracking data from source systems through transformations to final consumption by BI and AI systems.
• Own the data catalogue for modelled data layers, maintaining metadata, ownership, documentation, and sensitivity classifications in OpenMetadata or equivalent.

Stakeholder Enablement

• Work closely with data analysts, BI engineers, and AI engineers to understand their data needs and model data in ways that accelerate their workflows.
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