Role Overview:

A leading financial services organisation is seeking a senior technology leader to lead enterprise-wide data and artificial intelligence engineering and delivery. The role will own the development of secure, scalable data pipelines, reusable data products, and production-ready data capabilities supporting business applications, advanced analytics, and artificial intelligence initiatives. Reporting to the Chief Data and Artificial Intelligence Officer, the leader will translate enterprise strategy into execution, establish engineering standards, and drive delivery excellence across multiple teams. The role requires strong expertise in modern data architecture, artificial intelligence and machine learning enablement, data governance, and large-scale engineering delivery.

Key Responsibilities:

- Lead the enterprise data and artificial intelligence engineering function, including capability development, engineering standards, and delivery practices.

- Build and scale secure, reusable data pipelines, transformations, and data products to support business applications, analytics, and artificial intelligence outcomes.

- Establish engineering excellence through Agile, DevOps, DataOps, automation, and standardised delivery frameworks.

- Own end-to-end delivery, prioritisation, and execution across multiple engineering teams and workstreams.

- Partner with architecture, data governance, artificial intelligence portfolio teams, platform teams, and business stakeholders to align delivery with enterprise strategy.

- Ensure data quality, lineage, observability, security, privacy, and regulatory compliance across engineering outputs.

Ideal Candidate Profile:

- 20 - 28 years of experience in technology and engineering, with substantial leadership experience in enterprise data engineering and delivery.

- Strong banking or financial services background, with an understanding of financial data, fraud detection, and credit scoring.

- Demonstrated experience building and scaling enterprise data platforms, reusable data products, and large-scale data pipelines.

- Deep understanding of modern data architectures, including data lakes, data warehouses, lakehouse architecture, data fabric, streaming, and data mesh.

- Strong exposure to cloud-based data platforms, artificial intelligence and machine learning data enablement, and modern engineering practices.

- Proven ability to lead large engineering teams, manage complex delivery portfolios, and translate strategic priorities into production-ready solutions.