Data Engineering Lead

Job description

Job Summary: We’re looking for a Senior Data Engineer to take ownership of the data engineering layer that underpins enterprise reporting and analytics. This is a hands-on role focused on building and running the pipelines, transformations, curated datasets, and quality controls that turn operational data into trusted, usable assets for decision-making. You’ll play a key role in shaping how data engineering is done within a growing analytics function. In the near term, you’ll strengthen and evolve the platform foundations behind critical analytics use cases. Over time, you’ll help build more reusable, scalable data services that can support a broader range of analytics and digital needs. Key Responsibilities: Own and evolve core data pipelines, transformation logic, and curated datasets that support enterprise reporting and analytics. • Design, build, and maintain scalable data models across warehouse / lakehouse environments, with a focus on reliability, clarity, and reuse. • Implement strong data quality, validation, monitoring, and operational controls so critical data assets remain trusted and resilient. • Integrate data from multiple source systems into well-structured datasets for analytics and reporting use cases. • Work closely with analytics, BI, platform, and architecture colleagues to ensure downstream reporting and analytics sit on stable engineering foundations. • Apply strong engineering discipline through CI/CD, version control, documentation, and repeatable delivery patterns. • Improve performance, maintainability, and scalability of data pipelines and models as the platform grows. • Help establish reusable patterns and standards for data engineering across the analytics function. • Support the evolution of the analytics platform so it can serve not only reporting needs today, but broader analytics and digital use cases over time. What we’re looking for Essential experience / skills: • Strong senior-level data engineering experience building and maintaining scalable data platforms and pipelines. • Strong SQL plus Python / PySpark or equivalent experience for ingestion, transformation, and validation work. • Experience with cloud data platforms, orchestration tooling, and modern warehouse / lakehouse patterns. • Experience designing and maintaining curated datasets and data models for analytics use cases. • Experience implementing data quality, monitoring, validation, and secure / governed data handling. • Good engineering discipline, including CI/CD, version control, documentation, and repeatable delivery practices. • Ability to work with technical and non-technical stakeholders to translate business needs into robust technical solutions. Desirable experience / skills: • Experience with Microsoft Fabric, Azure-based data engineering, or similar modern cloud data environments. • Experience supporting analytics or enterprise reporting environments with high expectations around trust, governance, and continuity. • Familiarity with business-critical data domains such as finance, operations, or people data. • Experience helping teams raise engineering maturity through shared standards, patterns, and service ownership. Why join / opportunity • Take real ownership of important data assets at the heart of enterprise analytics. • Help shape how data engineering is done within a growing analytics capability. • Work on meaningful platform foundations that support trusted reporting today and more reusable analytics services over time. • Influence the move from fragmented data workflows toward more robust, scalable, and well-governed engineering patterns. • Partner with a broad set of stakeholders across analytics, BI, platform, and digital teams in a role with clear impact and room to grow. • Build something lasting: not just pipelines, but a stronger engineering foundation for future analytics delivery.