01Overview
Role Summary
As a Data Engineer in the Lakehouse and AI Data Platform team, you will design, build, test and support data pipelines and curated datasets on the firms modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.
The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.
Key Responsibilities
Pipeline Engineering
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.Where needed, build reusable tooling to improve delivery, consistency and operational support.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 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.
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.Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.
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, including where delivery would benefit from improvements to shared platform tooling.For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.
Skills and Experience
Required
7-12+ years of experienceBachelors or masters 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.
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 normalized and denormalized 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.Stronger ownership of technical design across multiple datasets or pipeline domains.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.
Technology Environment
The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.
Examples of technologies in scope include:
Data processing and logic: ANSI SQL, Apache Spark, KafkaData formats: JSON, Avro, ParquetPlatforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQEngineering and deployment: CI/CD tooling, containerized or Kubernetes-based deployment approaches where relevant
You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.
What We Are Looking For
We are
