Requirements

Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
Strong hands-on programming experience in Python or Java.
Good working knowledge of SQL, including troubleshooting, optimization and data analysis.
Ability to learn new tools, internal platforms and delivery workflows quickly.
Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.

What You'll Be Doing

Pipeline Engineering
- Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
- Refactor or modernize existing data flows where needed to improve reliability, performance and maintainability.
- Ensure data pipelines are production-ready, well-tested and operationally supportable.
Data Modelling and Curation
- Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
- Apply sound data modeling principles to represent business entities, relationships and historical change accurately.
- Work with consumers to shape data products that are usable, well-documented and aligned to business needs.
Data Quality and Reconciliation
- Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
- Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
- Contribute to clear standards for testing, monitoring and issue resolution.
Delivery and Partnership
- Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
- Communicate clearly on progress, risks, dependencies and design choices.
- Take a broader role in technical leadership, task breakdown and support for junior engineers.

Experience guiding implementation standards, code quality and engineering practices within a team.
Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.
Understanding of temporal data modeling, including the handling of historical state and change over time.
Knowledge of schema design, schema evolution and data compatibility considerations.
Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
Ability to make sensible design choices across normalized and denormalized models, and between natural and surrogate keys.
Practical approach to data quality, reconciliation and root-cause analysis.