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Global brand, mid-level generalist role, metro location, and broad data-stack requirements increase candidate competition.
Data engineering skills are broadly transferable across industries, though platform and tooling experience moderately matter.
Explicit 5+ years, required lead experience, and specific modern data-stack plus cloud/DevOps skills indicate strict filters.
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Lead a cross-functional team of data engineers, owning architecture decisions and day-to-day team activities like sprint planning and backlog management.
Develop and maintain enterprise-scale data engineering solutions including data ingestion, processing, integration, and governance to meet business needs.
Drive platform stability, DevOps adoption, operational SLAs, and contribute to community-of-practice initiatives including onboarding, upskilling, and shared frameworks.
Bachelor's degree or higher in Computer Science, Statistics, Business, Information Technology, or related field.
5+ years experience in data engineering or related discipline with at least 2 years in a technical lead or team lead role.
Proficiency in Python, advanced SQL including window functions and query optimisation; experience with dbt preferred.
Experience with cloud platforms and DevOps (Azure, AWS, or GCP), Agile methodologies, automated testing, and data testing frameworks.
Experienced leader comfortable owning end-to-end architecture and delivery of data engineering projects in enterprise environments.
Proven ability to develop shared frameworks and data assets for mission-critical applications, with knowledge of data product concepts like lineage and certification.
Pragmatic operator who can drive team growth, collaborate across business and technical stakeholders, and implement data quality frameworks and pipeline optimisation.