We are easyJet – a FTSE-250 listed, £multi-billion low-cost airline that serves tens of millions of customers every single year. If you’re reading this, you have probably already been an easyJet customer, and you’ll know that there is no more iconic (or Orange!) travel brand in Europe.
We fly more than 1,207 routes, connecting 38 countries across Europe, and employ more than 18,000 colleagues. We’re on a mission to make low-cost travel easy – and whatever your role here, you’ll connect millions of people to what they love using Europe’s best airline network, great value fares, and friendly service.
What makes us easyJet? Our Promise Behaviours – we are Safe, Bold, Welcoming and Challenging. Four Behaviours. One Spirit. One easyJet.
JOB PURPOSE
The Lead Data Engineer is responsible for the technical direction, design, delivery and production implementation of analytical data solutions across the IT and Data estate. Combining exceptional technical capability with inspirational leadership, the role shapes the Analytics Engineering practice and ensures that data is fit for purpose, curated for consumption and discoverable within and beyond individual business domains.
Working with Data Engineering leadership, cross-functional product teams and wider stakeholders, the role leads the development of high-quality, reusable analytical pipelines and data products that enable reporting, decision support, data science, machine learning and AI. The role also develops engineers, sets technical standards and helps easyJet progress towards being a leading data-driven airline
JOB ACCOUNTABILITIES
> Serve as the technical authority for designing and delivering production-grade analytical data solutions and reusable data products across multiple business domains.
> Shape and manage the future direction of the Analytics Engineering practice, defining standards, patterns and approaches for critical analytical pipelines and curated data assets.
> Champion the distributed data platform and drive Data Product adoption, ensuring assets are catalogued, documented and discoverable so consumers understand what is available, where it exists and how to access it.
> Lead the transformation of data through the silver and gold layers, ensuring datasets are structured, modelled and optimised for analytics, BI, data science, machine learning and AI use cases.
> Own engineering quality across analytical pipelines by embedding automated testing, validation, observability, end-to-end lineage, performance optimisation and appropriate production support.
> Ensure data products comply with data management, security, privacy and governance requirements, working collaboratively with Business Data Stewards.
> Partner with BI Analysts, Data Scientists and business stakeholders to refine requirements and analytical methods, create production-ready feature engineering and deliver meaningful cross-functional datasets.
> Provide direct line management, coaching and technical guidance to Data Analytics Engineers, supporting personal development and ensuring work meets required engineering standards.
> Work within cross-functional teams of internal and external colleagues to deliver enterprise-scale solutions, clearly communicating technical concepts and designs to technical and non-technical audiences.
> Verify requirements; define data engineering components; establish implementation, testing and QA approaches; manage technical debt; and ensure functional and non-functional requirements are met.
> Identify emerging technologies, methods and techniques across data engineering and analytics engineering, assessing and championing those that offer practical business value.
> Work with Data Engineering leadership to improve delivery processes, automate manual activity and optimise ways of working.