Lead Data Engineer

In this senior, hands-on role you will anchor data engineering work for an enterprise platform, driving end-to-end delivery of robust data pipelines and services. You’ll operate across legacy and cloud environments to improve real-world data systems, guiding teammates through design decisions and technical delivery. The role emphasizes practical problem-solving, data quality, and governance in a fast-paced, complex setting. You’ll work closely with stakeholders to translate operational challenges into scalable solutions and help raise engineering standards.

• Design, build, and enhance enterprise-scale data pipelines, platforms, and data services
• Lead end-to-end data engineering work from problem definition to operational use
• Collaborate with technical and non-technical stakeholders to translate problems into data solutions
• Analyse and synthesize data from multiple sources to shape structure, validation, and consumption
• Provide technical leadership through design decisions, code, and delivery support
• Improve data sharing, onboarding of new sources, and interoperability across teams
• Evaluate and adopt tools and patterns that provide genuine value while balancing pragmatism
• Develop robust, production-grade solutions and address metadata, lineage, and data quality as core concerns
• Diagnose and resolve complex data and platform issues in constrained environments
• Contribute to an inclusive engineering culture with high technical standards

• Hands-on experience as a Senior or Lead Data Engineer on complex, real-world systems
• Strong Python and Spark skills, with production data pipelines experience
• Deep understanding of the full data engineering lifecycle (ingestion, transformation, storage, serving, reuse)
• Experience designing data integrations with diverse sources and legacy systems
• Ability to design data models for analysis, operational use, and maintainability
• Knowledge of data security, compliance, and governance integrated into systems
• Experience with established data development practices (version control, testing, deployment, change management)
• Clear communication between technical and non-technical stakeholders
• Persistence tackling messy data and organizational complexity

• Clear communication with technical and non-technical stakeholders
• Persistence and resilience in challenging environments
• Collaboration and teamwork
• Python
• Apache Spark
• End-to-end data pipeline development