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, optimisation 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.

Data Engineering Capability

Understanding of temporal data modelling, 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 normalised and denormalised models, and between natural and surrogate keys.
Practical approach to data quality, reconciliation and root-cause analysis.
Experience building or supporting production data pipelines in a collaborative engineering environment.
Experience working with distributed data processing frameworks such as Apache Spark.
Working knowledge of common data formats such as JSON, Avro and Parquet.

Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
Ensure data pipelines are production-ready, well tested and operationally supportable.
Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
Apply sound data modelling 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.
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.
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.