Data engineer for reporting and analytics

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.

• 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.

• 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.
• Detail-oriented with a commitment to code quality and documentation; proactive in identifying modelling gaps and quality issues.
• Proven ability to work effectively within cross-functional squads and alongside third-party resources.
• Understanding of data quality principles, including validation, monitoring, alerting, and resolution.
• 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.
• Please note that this role requires you to be based in our Central London office four days a week.
• Будет плюсом: Experience working alongside or transitioning from outsourced (e.g., EPAM) delivery models is beneficial

• Условий в вакансии нет