Introduction

At Burberry, we believe creativity opens spaces. Our purpose is to unlock the power of imagination to push boundaries and open new possibilities for our people, our customers, and our communities. This is the core belief that has guided Burberry since it was founded in 1856 and is central to how we operate as a company today.

We aim to provide an environment for creative minds from different backgrounds to thrive, bringing a wide range of skills and experiences to everything we do. As a purposeful, values-driven brand, we are committed to being a force for good in the world, creating the next generation of sustainable luxury for customers, driving industry change, and championing our communities.

Job Purpose

The Data Engineer is accountable for the data products that underpin Burberry's reporting and analytics. Operating within cross-functional squads, the role works alongside Data Product Managers, Data Platform Engineers, Visualisation & Reporting Engineers, architects, and third-party resources.

Demand is routed through Data Product Managers and product teams. As part of the transition from partner-led to internally owned delivery, the role carries accountability for knowledge retention, documentation standards, and engineering consistency within the data product engineering layer.

Accountability Boundaries and Key Interfaces

• Accountable for transformed, modelled, and governed data products from ingested platform data through to business-ready data layers.
• Not accountable for platform infrastructure operations, enterprise platform architecture, or final report/dashboard build, except where support is needed to define clean handoffs.
• Key interfaces include Data Product Managers, Data Platform Engineering, Visualisation & Reporting, Data Governance, Solution Architecture, and third-party delivery partners.

Responsibilities

• Design and build data models and transformation logic to turn ingested data into governed products across domains such as Customer, Product, Order, Sale, and Supply Chain.
• Manage the engineering layer between platform-level ingestion and reporting/visualisation output to ensure data is consumable to enterprise standards.
• Collaborate with Data and Solution Architects to ensure work aligns with enterprise data models and platform strategy.
• Work with Data Platform Engineers to consume data from the enterprise platform (Databricks), applying business logic to create clean, reusable products.
• Provide governed data products to the Visualisation & Reporting team, ensuring alignment with enterprise data definitions and the business glossary.
• Embed quality controls, validation, testing, and monitoring into the transformation layer by design.
• Maintain clear documentation of business rules, data lineage, and transformation logic to support team-wide consistency.
• Facilitate the shift from third-party-led to internal ownership by participating in knowledge transfer and establishing in-house engineering standards.
• Function within a squad-based delivery model, dynamically allocated to cross-functional squads based on prioritised demand.
• Work with Data Product Managers to understand business requirements and translate them into technically sound data engineering outputs.
• Define and maintain interface contracts between data engineering outputs and the reporting/visualisation layer — ensuring clean handoffs to Visualisation & Reporting Engineers.
• Support the productionisation of data science outputs where required, taking models or analyses developed in the business and engineering them into scalable, governed data products.
• Support L2/L3 data pipeline incidents where required, investigating and resolving data quality or pipeline failure issues in collaboration with the Data Platform Engineer (for infrastructure-level issues).
• Contribute to the continuous improvement of data engineering practices, reusable patterns, and team knowledge.
• Apply consistent engineering practices including Git branching, peer review, reusable components, automated testing, CI/CD quality gates, and clear code ownership.
• Define and maintain data contracts, data product versioning, and semantic-readiness requirements so downstream teams have stable and predictable consumption points.
• Embed privacy, PII handling, retention, and access-control requirements into transformation logic in line with Data Governance, Cyber Security, and platform guardrails.
• Support master and reference data handling, slowly changing dimensions, and reusable dimensional/medallion modelling patterns where required by enterprise data products.