Location
London

Hours
Full Time

Salary
Negotiable

About the Role
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, customers, and communities. 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, driving industry change, and championing our communities.

The Data Engineer is accountable for the data products that underpin Burberry's reporting and analytics. Operating within cross-functional squads, this role collaborates with Data Product Managers, Data Platform Engineers, Visualisation & Reporting Engineers, architects, and third-party partners. The role carries accountability for knowledge retention, documentation standards, and engineering consistency within the data product engineering layer as part of the transition from partner-led to internally owned delivery.

Key responsibilities include designing and building data models and transformation logic to create governed data products across domains such as Customer, Product, Order, Sale, and Supply Chain. You will manage the engineering layer between platform-level ingestion and reporting/visualisation output to ensure data is consumable to enterprise standards. Collaboration with Data and Solution Architects ensures alignment with enterprise data models and platform strategy.

You will work closely with Data Platform Engineers to consume data from the enterprise platform (Databricks), applying business logic to create clean, reusable products. Providing governed data products to the Visualisation & Reporting team, embedding quality controls, validation, testing, and monitoring into the transformation layer by design is essential.

Maintaining clear documentation of business rules, data lineage, and transformation logic supports team-wide consistency. You will facilitate the shift from third-party-led to internal ownership by participating in knowledge transfer and establishing in-house engineering standards.

Functioning within a squad-based delivery model, you will work with Data Product Managers to understand business requirements and translate them into technically sound data engineering outputs. Defining and maintaining interface contracts between data engineering outputs and the reporting/visualisation layer ensures clean handoffs.

Supporting the productionisation of data science outputs, resolving L2/L3 data pipeline incidents, contributing to continuous improvement of data engineering practices, and applying consistent engineering practices such as Git branching, peer review, automated testing, and CI/CD quality gates are key aspects of this role.

Embedding privacy, PII handling, retention, and access-control requirements in line with Data Governance, Cyber Security, and platform guardrails is critical. You will also support master and reference data handling, slowly changing dimensions, and reusable dimensional/medallion modelling patterns as required.

Experience
- Proven experience in a data engineering role building models and transformation layers in a modern, cloud-based environment (e.g., Databricks).
- Proficiency in Python, SQL, and Spark with practical experience in ETL/ELT processes and data modelling.
- Experience developing and working with CI/CD pipelines.
- Experience developing and applying metadata-driven data ingestion frameworks.
- Solid understanding of dimensional/relational modelling, data quality management, and integration patterns.
- Familiarity with data governance principles, metadata standards, and business glossary alignment.
- Experience working effectively within cross-functional squads and alongside third-party resources.
- Experience with lakehouse and medallion architecture patterns, Delta/Parquet-based data products, semantic model readiness, and data product lifecycle management.
- Strong software engineering discipline including source control, peer review, unit/integration testing, deployment automation, and production support practices.
- Working understanding of privacy, access control, data retention, and audit requirements for enterprise data products.

About you
- Detail-oriented with a commitment to code quality and documentation.
- Proactive in identifying modelling gaps and quality issues.
- Proven ability to collaborate within dynamic, squad-based delivery models.
- Experience transitioning from outsourced delivery models is beneficial.
- Strong problem-solving skills with the ability to investigate and resolve data quality or pipeline failure issues.
- Committed to continuous improvement of data engineering practices and reusable patterns.

Qualifications
- Relevant technical qualifications or equivalent practical experience in data engineering or related fields are preferred but not explicitly required.