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Data & AI Modeler

Boundless
Dubai, UAE
Full Time
Mid
2 weeks ago
Data ModelingKimball MethodologyData Vault 2.0SQLdbtDatabricks
Free

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Data ModelingKimball MethodologyData Vault 2.0
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Company Overview

  • Our client is a global, regulated financial services group with a strong international presence across trading, fintech, digital assets, and technology led financial products.
  • Headquartered in Dubai, the organisation operates across multiple jurisdictions and serves a large global client base through advanced, secure, and scalable digital platforms.

Role Overview

  • We are seeking a Data & AI Modeler to own the design, implementation, and governance of enterprise data models that support AI systems, business intelligence, analytics, reporting, and machine learning initiatives.
  • This is a specialist role at the intersection of data architecture and AI enablement.
  • The successful candidate will design scalable dimensional models, Data Vault schemas, semantic layers, and feature data foundations that ensure data is clean, governed, reusable, and semantically consistent across the organisation.

Data Modelling & Architecture

  • Design and maintain enterprise data models across core business domains, including customers, transactions, products, trading activity, events, financial data, and operational processes.
  • Develop dimensional models using Kimball methodologies, including star schemas, snowflake schemas, and wide denormalised models tailored to different analytical and reporting needs.
  • Design and implement Data Vault 2.0 models, including hubs, links, satellites, and business vault structures.
  • Partner with Data Engineers to ensure data models are implemented efficiently and scalably across cloud data platforms such as Databricks, Snowflake, BigQuery, or Redshift.
  • Ensure data models are extensible, well documented, performant, and suitable for both BI and AI consumption.

Semantic Layer & Metric Governance

  • Design and maintain governed semantic and metrics layers using dbt Metrics, LookML, or equivalent technologies.
  • Establish canonical definitions for business metrics, KPIs, dimensions, hierarchies, calculations, and business logic.
  • Ensure key business metrics are documented, auditable, consistently applied, and reused across BI dashboards, reporting, analytics, and AI systems.
  • Work with BI and Analytics teams to expose governed datasets through reporting tools with appropriate access controls, definitions, and documentation.
  • Reduce inconsistencies in reporting by creating a trusted, centralised data layer for enterprise consumption.

AI & Machine Learning Data Architecture

  • Design data foundations for machine learning and AI use cases, including reliable feature datasets for model training, validation, inference, and monitoring.
  • Build point in time correct datasets that prevent data leakage and support reproducible model development.
  • Support feature store design and integration using technologies such as Feast, AWS SageMaker Feature Store, or custom feature platform solutions.
  • Enable both offline historical feature generation and online, low latency feature serving where required.
  • Work closely with AI Engineers and Data Scientists to ensure training datasets, labels, features, and transformations are version controlled and production ready.

Data Quality, Governance & Lineage

  • Implement automated data quality frameworks using dbt tests, Great Expectations, and related technologies.
  • Define and monitor quality controls covering schema validation, null rates, referential integrity, freshness, duplication, reconciliation, and business rule validation.
  • Build and maintain end to end lineage documentation from source systems through transformation layers to BI, reporting, AI, and ML consumption.
  • Maintain metadata, ownership, documentation, sensitivity classification, and governance standards across modelled datasets.
  • Support the implementation and ongoing management of data cataloguing platforms such as OpenMetadata, Alation, Atlan, or equivalent tools.

Stakeholder Enablement

  • Partner with Data Analysts, BI Engineers, AI Engineers, Product teams, and business stakeholders to understand data requirements and translate them into scalable model design.
  • Provide clear documentation, training, and guidance that enables self service access to trusted and well modelled data.
  • Explain data model decisions, metric definitions, and governance standards clearly to both technical and non technical stakeholders.
  • Promote data modelling, data governance, and semantic consistency best practices across the organisation.

Requirements

  • 4–9 years of experience in Data Modelling, Data Warehousing, Data Architecture, Analytics Engineering, or a similar role.
  • Strong practical experience designing dimensional models, including star schemas, snowflake schemas, fact tables, dimensions, and slowly changing dimensions.
  • Hands on experience with Data Vault 2.0 methodologies, including hubs, links, satellites, and business vault concepts.
  • Advanced SQL skills with experience writing production grade transformation logic.
  • Strong experience with dbt, including models, macros, packages, tests, snapshots, documentation, and metrics.
  • Experience designing and maintaining semantic layers, metrics layers, or governed analytical datasets for BI consumption.
  • Understanding of machine learning data requirements, including feature engineering, feature stores, point in time correctness, temporal splits, data leakage prevention, and reproducible pipelines.
  • Experience working with modern cloud data platforms such as Databricks, Snowflake, BigQuery, Redshift, or equivalent.
  • Exposure to lakehouse and modern data storage formats, including Delta Lake, Apache Iceberg, and Parquet.
  • Experience implementing data quality controls using dbt tests, Great Expectations, or similar tools.
  • Familiarity with metadata management, data cataloguing, lineage, and governance platforms such as OpenMetadata, Alation, Atlan, OpenLineage, or Marquez.
  • Strong stakeholder management and communication skills, with the ability to explain complex data concepts clearly to technical and business audiences.

Core Technical Skills

  • Data Modelling: Kimball methodology, star schema, snowflake schema, Data Vault 2.0, wide tables, semantic models
  • Data Transformation: SQL, dbt, advanced macros, packages, snapshots, testing, documentation
  • Data Platforms: Databricks, Snowflake, BigQuery, Redshift
  • Storage Technologies: Delta Lake, Apache Iceberg, Parquet
  • Semantic & BI Layers: dbt Metrics, LookML, governed metrics layers, dimensional hierarchies
  • Data Quality: Great Expectations, dbt tests, reconciliation, integrity validation, monitoring
  • Governance & Metadata: OpenMetadata, Alation, Atlan, lineage, cataloguing, data ownership
  • AI & ML Data Foundations: feature engineering, feature stores, Feast, SageMaker Feature Store, training datasets, inference datasets, data leakage prevention

What Sets This Role Apart

  • Own the data foundation that supports enterprise wide AI, analytics, reporting, and decision making capabilities.
  • Shape the modelling standards, semantic governance, and data quality practices used across the organisation.
  • Work across both BI and AI/ML data architectures, creating a rare combination of analytical and AI enablement expertise.
  • Play a critical role in ensuring AI systems, dashboards, and analytics products are built on trusted, scalable, and well governed data.

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