Data Engineering Lead

Overview

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

• 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
• Attention to data quality and governance
• SQL
• Python / PySpark
• Cloud data platforms