A senior, hands-on data engineering lead who owns and evolves the pipelines, transformations, and curated datasets behind enterprise reporting and analytics, working with analytics and platform teams. Core stack includes SQL, Python/PySpark, cloud warehouse/lakehouse platforms, orchestration, data quality tooling, and CI/CD.
In this role you will own and evolve the data engineering layer that underpins enterprise reporting and analytics. You will build and operate pipelines, transformations, and curated datasets to turn operational data into trusted assets for decision-making. You’ll shape platform foundations to support current analytics needs and, over time, scalable data services for broader digital use cases. You’ll work with cross-functional teams to ensure robust, well-governed data engineering practices and repeatable delivery. This is a hands-on, impact-driven senior role with a clear opportunity to influence the analytics platform’s future.
Responsibilities
Own and evolve core data pipelines, transformations, and curated datasets for enterprise reporting and analytics
Design, build, and maintain scalable data models across warehouse/lakehouse environments with emphasis on reliability and reuse
Implement data quality, monitoring, and operational controls to keep data assets trusted
Integrate data from multiple sources into analytics-ready datasets
Collaborate with analytics, BI, platform, and architecture teams to ensure stable foundations for downstream reporting
Apply CI/CD, version control, and documentation to ensure repeatable delivery
Improve performance, maintainability, and scalability of pipelines and models as the platform grows
Help establish reusable patterns and standards for data engineering across analytics
Key requirements
Strong senior-level data engineering experience building scalable data platforms and pipelines
Strong SQL plus Python / PySpark or equivalent for ingestion, transformation, and validation
Experience with cloud data platforms, orchestration tooling, and modern warehouse/lakehouse patterns
Experience designing and maintaining curated datasets and data models for analytics
Experience implementing data quality, monitoring, validation, and governed data handling
Good engineering discipline including CI/CD, version control, and documentation
Ability to translate business needs to robust technical solutions with stakeholders
Stakeholder collaboration
Communication with both technical and non-technical audiences