Head - Data Engineering
First seen by Alion on Oct 23, 2026.
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.
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.
Skills
Data Engineering, Artificial Intelligence, Machine Learning, Data Governance, DataOps, DevOps, Data Architect, Enterprise Data Hub