About MultiBank Group

 

MultiBank Group is a global leader in financial derivatives and trading technology, with over US$18 billion in daily volume and clients across 100+ countries. As we expand our data-driven culture, our mission is to create a unified Data Platform — a single source of truth empowering business intelligence, personalization, and AI-driven decisioning across all brands and entities.

 

Position Overview

 

We are seeking a visionary Head of Data Technology to architect, lead, and deliver the MultiBank Group Data Platform. This role will define the technology foundations of our data ecosystem, integrating cross-brand data sources into a unified Lakehouse architecture and enabling the next generation of AI, analytics, and personalisation capabilities.

This is a foundational leadership position — responsible for technology strategy, architecture design, and execution of all data infrastructure projects, from ingestion to visualization. You will manage a team of Data Engineers, BI Developers, and Cloud Specialists, and collaborate closely with Product, AI, and Business stakeholders to deliver reliable, scalable, and governed data solutions.

 

Key Responsibilities

Leadership & Strategy

  • Lead the end-to-end design and delivery of the MultiBank Group Data Platform aligned with the Group CTO’s vision.
  • Build and scale a world-class data technology team across engineering, analytics, and DevOps disciplines.
  • Own data architecture decisions — including the Lakehouse model, S3 storage layers, Glue/DBT transformations, Kafka/Spark ingestion, and Redshift/RDS clusters.
  • Collaborate with BI, AI, and Product leadership to define data standards, governance, and best practices.
  • Drive adoption of self-service analytics, data observability, and data quality frameworks across departments.

 

Platform Architecture & Delivery

  • Architect and implement the data ingestion, transformation, and modeling layers for all business verticals (Trading, CRM, Marketing, Compliance).
  • Design and manage real-time data pipelines using Kafka and Spark Streaming.
  • Oversee data modeling, schema design, and versioning in line with dbt, Glue, and Lakehouse practices.
  • Ensure data availability, consistency, and reliability across systems through monitoring, alerting, and SLAs.
  • Lead migration and modernization initiatives on AWS (S3, Glue, RDS, Redshift, SageMaker).
  • Integrate data systems with AI/ML infrastructure to enable personalization, customer segmentation, and risk analytics.

 

Governance & Security

  • Implement data access policies, audit trails, and lineage for compliance with regulatory and privacy frameworks (GDPR, UAE PDPL).
  • Partner with the cybersecurity team to enforce encryption, key management, and data loss prevention.
  • Establish CI/CD practices for data pipeline deployment and version control.

 

Monitoring & Reliability

  • Implement 24/7 observability for all data pipelines and warehouse systems using tools such as CloudWatch, Datadog, or Prometheus.
  • Create dashboards for data quality KPIs, ingestion latency, and SLA compliance.
  • Lead incident management and continuous improvement initiatives.
  • Required Knowledge and Experience
  • 10+ years in data engineering, platform architecture, or infrastructure roles, including 3+ years in leadership.
  • Deep expertise in AWS data stack (S3, Glue, Redshift, RDS, SageMaker, IAM).
  • Strong command of Kafka, Spark, Airflow, dbt, and containerised data services (Docker/Kubernetes).
  • Experience with data modeling, ETL automation, and version-controlled transformation layers.
  • Demonstrated success leading data platform or modernisation projects from concept to production.
  • Proven ability to bridge technical and business stakeholders.

 

Desirable Certifications

  • AWS Certified Data Analytics – Specialty
  • Databricks Certified Data Engineer Professional
  • Certified Data Management Professional (CDMP)
  • PMP or Agile certification (for delivery management)

 

Collaboration

  • Works cross-functionally with Product, AI, and Cybersecurity teams to enable data-driven decisions.
  • Strong leadership, mentoring, and communication skills to scale a multi-disciplinary data team.
  • Passion for data culture, innovation, and measurable business outcomes.