Location
London

Hours
Full Time

Salary
Negotiable

About the Role
At Burberry, creativity opens spaces and drives us to push boundaries and unlock new possibilities for our people, customers, and communities. As a Data Engineer, you will be accountable for the data products that underpin Burberry's reporting and analytics. Working within cross-functional squads, you will collaborate with Data Product Managers, Data Platform Engineers, Visualisation & Reporting Engineers, architects, and third-party partners to deliver high-quality, governed data products.

Your responsibilities include designing and building data models and transformation logic to convert ingested platform data into business-ready, governed data products across domains such as Customer, Product, Order, Sale, and Supply Chain. You will manage the engineering layer between platform ingestion and reporting outputs, ensuring data is consumable to enterprise standards and aligned with enterprise data models and platform strategy.

You will embed quality controls, validation, testing, and monitoring into the transformation layer by design, maintain clear documentation of business rules, data lineage, and transformation logic, and facilitate the transition from third-party-led to internal ownership by establishing engineering standards and knowledge transfer.

Operating within a squad-based delivery model, you will translate business requirements into technically sound data engineering outputs, define and maintain interface contracts with reporting teams, support productionisation of data science outputs, and contribute to continuous improvement of data engineering practices. You will also support incident resolution for data pipelines and embed privacy, PII handling, retention, and access-control requirements in line with governance and security standards.

This role requires a strong software engineering discipline, including source control, peer review, automated testing, CI/CD, and production support practices, ensuring stable and predictable data product consumption for downstream teams.

Experience
- Proven experience in a data engineering role building models and transformation layers in modern, cloud-based environments such as 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 and metadata-driven data ingestion frameworks.
- Solid understanding of dimensional and 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 delivery partners.
- Knowledge of 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.
- 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.
- Able to collaborate effectively with diverse teams and adapt to dynamic squad-based delivery models.
- Experience transitioning from outsourced delivery models is beneficial.
- Strong problem-solving skills to investigate and resolve data quality or pipeline issues.

Qualifications
- Relevant technical qualifications or equivalent practical experience in data engineering or related fields.
- Continuous learning mindset with a passion for improving data engineering practices and standards.