Head of Data & AI

Trade Nation is a multi-jurisdictional CFD and spread betting broker with regulated entities across the UK, Australia, the EU, the Bahamas, and Seychelles. They focus on providing low-cost trading services powered by technology with teams distributed globally.

• Proven leadership experience within Data, Analytics, or AI functions
• Demonstrable success implementing AI solutions that have transformed business workflows and delivered measurable impact
• Strong track record of delivering projects from concept through to production and adoption
• Hands-on expertise in SQL, data modelling, and analytics engineering
• Experience with dbt or similar version-controlled transformation frameworks
• Strong understanding of data warehouse architecture and semantic modelling
• Experience working with Redshift, Snowflake, BigQuery, or similar cloud data platforms
• Background in financial services, trading, brokerage, or capital markets
• Experience leading, coaching, and developing analytics teams
• Excellent stakeholder management and communication skills with both technical and non-technical audiences

Nice to have:

• Experience with CFD, spread betting, or brokerage platforms
• Knowledge of trading systems and back-office financial services data structures
• Experience in multi-entity, multi-regulator environments
• Exposure to regulatory reporting frameworks such as MiFID II, MiFIR, or ASIC requirements
• Experience implementing AI solutions within regulated financial services environments

• Own and drive Trade Nation's AI strategy from opportunity identification through to deployment, adoption, and value realisation
• Redesign business workflows around AI, focusing on practical outcomes rather than tool adoption
• Build AI capability through training, prompt libraries, templates, and a network of AI champions
• Measure and report AI adoption, success metrics, and lessons learned
• Own the business-facing gold layer of the data warehouse, including metrics, definitions, and quality standards
• Develop and extend the semantic data model to ensure consistent, trusted access to business data
• Champion data self-service, empowering teams to answer their own questions efficiently
• Establish and maintain best practices for analytics engineering, including testing, version control, documentation, and code reviews
• Simplify and improve the quality of the data estate
• Deliver proactive insights and recommendations to senior leadership