Data Engineer

Overview

In this role you will design and deliver governed data products within Burberry’s data platform, partnering with cross-functional squads to enable reliable reporting and analytics. You will shape data models and transformation logic from ingested data to business-ready outputs, ensuring alignment with enterprise standards and governance. You will collaborate with data and solution architects, platform engineers, and governance teams, supporting internal ownership and scalable data products. This role offers the chance to influence data maturity, enable data-driven decisions, and contribute to Burberry’s sustainable luxury mission.

Responsibilities

• Design and build data models and transformation logic to produce governed data products across domains (Customer, Product, Order, Sale, Supply Chain)
• Manage the engineering layer between data ingestion and reporting to ensure enterprise-ready data
• Collaborate with Data and Solution Architects to align with enterprise models and platform strategy
• Consume data from the enterprise platform (Databricks) and apply business logic to create clean, reusable products
• Provide governed data products to the Visualization & Reporting team with consistent definitions and glossaries
• Embed quality controls, validation, testing, and monitoring in the transformation layer
• Maintain documentation of business rules, data lineage, and transformation logic
• Support the shift from third-party to internal ownership via knowledge transfer and standards
• Work in a squad-based delivery model and translate business requirements into technical data outputs
• Define interface contracts between data engineering outputs and reporting/visualisation layers
• Support productionisation of data science outputs as scalable data products
• Assist with L2/L3 data pipeline incidents and data quality issues in collaboration with Data Platform Engineers
• Contribute to continuous improvement of data engineering practices and reusable patterns
• Apply engineering practices including Git, peer review, automated testing, CI/CD, and clear code ownership
• Define and maintain data contracts, versioning, and semantic readiness for downstream teams
• Embed privacy, PII handling, retention, and access-control requirements in line with governance and guardrails
• Support master/reference data handling and dimensional/medallion modelling as required

Key requirements

• Experience in data engineering in a cloud-based environment (e.g., Databricks)
• Proficiency in Python, SQL, and Spark; ETL/ELT and data modelling experience
• Experience with CI/CD pipelines
• Experience with metadata-driven data ingestion frameworks
• Strong understanding of dimensional/relational modelling and data quality management
• Familiarity with data governance principles, metadata standards, and business glossary alignment
• Detail-oriented with focus on code quality and documentation
• Proven ability to work in cross-functional squads and with third-party resources
• Understanding of data quality principles including validation, monitoring, and alerting
• Experience with lakehouse and medallion architectures, Delta/Parquet-based data products, semantic models, and data product lifecycle
• Strong software engineering discipline: source control, peer reviews, tests, deployment automation, production support
• Awareness of privacy, access control, data retention, and audit requirements for enterprise data products
• Experience working with or transitioning from outsourced delivery models (e.g., EPAM) is beneficial

• Collaborative
• Detail-oriented
• Proactive in identifying gaps
• Databricks
• Python
• SQL

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